نشریه علوم و فنون نقشه برداری
سال پنجم شماره 1 (پیاپی 17، امرداد 1394)

The rapid growth of location-based social networks through attracting millions users، reveal the high popularity in the short time period. A result of the location based social network services، access to a large collection of data that can extract the spatial history، social relationships structure، movement behavior and characteristics of users. Social، spatial and temporal data analysis leads to create a wide range of location based services. By using statistical and knowledge discovery techniques for advising unvisited locations to users and decreasing large data volume problems، recommendation systems have become the popular services of these networks. Recommendation system is an approach to deal with the problems caused by large amount and growing volume of information and helps user to approach his/her goal among the enormous amount of information. In this paper، we design the novel GEO-FIF method to recommend unvisited places to tourists based on their location history. This time، location and preference aware recommendation system، offer a set of locations near to the user's current position with regard to the time term and the geographical distance between end user and other users as well user preferences that is extracted from visited locations. In the first stage، we survey the impact of the distance between users on common visited locations. Then we measure users’ interest into the locations by creating a user – location matrix within the period of one day and utilizing the content based filtering that for each user calculates a score for each place. In the next step by having the current position of the user، collection of places is limited according to their distance from the user. This is in addition to speed up processing and computing، increase recommendation accuracy. Finally، by using an innovative function، we estimate the similarity of the target user and other users based on the combination of the distance between them and the given score to places by other users. The score of a place for the target user is calculated by accumulative scores given by other users. In fact، we utilize collaborative filtering method to measure the similarity between users and predict the user interest to a new location based on accumulative scores of similar users. Finally combining these methods provides k unvisited locations within specific distance to user’s real-time location and its current time period. In this paper، Gowalla check-in data for Beijing، China were used in the period between October 2011 and November 2011. To evaluate the performance of the proposed method، the results of this method were compared with the results of two basic recommendation system methods in terms of rank accuracy indicator which is most common method for assessing recommender systems. The proposed method increases precision 15 and 12 percent in compared to user-based collaborative filtering using binary methods and GM-FCF، respectively.

In GIS, data is organized in themes as spatial layers. One of the most significant tasks of GIS is to analysis, spatial layers in order to model spatial phenomena. Since spatial data in GIS are inherently uncertain, a system that handles and infers from such uncertain data is of vital importance. Insufficient consideration to spatial modeling can lead to several problems in spatial decision making, death toll, property damage, financial loss and other hardships.There is no analytical solution in most spatial modeling. For these modeling, methods inspired by nature sometimes work very efficiently and effectively. These biologically inspired methods are called Soft Computing. The modeling of mineral potential is one of the special cases of the problem of spatial modeling in which no comprehensive model has been developed for it. A mineral potential mapping which depicts the favorability of mineralization occurring over a specified area is an important process for mineral deposit exploration. The purpose of this paper is to suggest several soft computing methods such as a fuzzy inference system (FIS), neural networks and genetic algorithms in a GIS framework for mineral potential mapping.A typical FIS is composed of two main parts: The Knowledge Base (KB) and the Inference System. The KB composed of Data Base (DB) and Rule Base (RB) stores the available knowledge about in the form of linguistic “IF-THEN” rules. One of the major problems in constructing an FIS is to build the knowledge base. In the conventional design methods, the desired rules and functions are based on the expert's knowledge and experiences. However, we cannot perfectly represent the expert's knowledge by linguistic rules nor choose appropriate membership functions for fuzzy sets. Moreover, converting the expert's knowledge into if-then rules is difficult and often results are incomplete, unnecessary and include conflicting knowledge, since experts cannot express all their knowledge. These problems can be sorted out applying techniques to construct a fuzzy knowledge base of numerical input-output data. In this research, two methods of dividing the input-output spaces and neural network are implemented to deal with this problem.To evaluate the constructed FIS, the genetic neural network has been applied and its results compared to the results of the FIS. In a genetic neural network, genetic algorithm is used for neural networks in order to optimize the network architecture. As a matter of fact, the topology of the networks is encoded as a chromosome and some genetic operators are applied to find an architecture which fits best the specified task according to some explicit design criteria.Numerical experimentations showed that the genetic neural network used in this study is the most successful method. It could predict the characteristic of 85% boreholes correctly. The results also indicated that genetic neural network and FIS with RMSE of 4 and 15 respectively are more accurate methods.

In the recent decades, raster based cellular automata (CA) models have been used increasingly in simulating land-use changes and urban growth because of simplicity of the computation and their conformity with remote sensing data. Current researches have introduced vector based cellular automata to tackle with some limitations that raster based conventional cellular automata models are faced with, such as sensitivity to spatial scale. The purpose of this study is modelling land use suitability to simulating land use changes through vector based cellular automata with application of cadastral parcels instead of pixel. In the proposed model, urban land use suitability is evaluated based on physical suitability, accessibility and neighborhood effect. Two parameters of altitude and slope are combined together by weighted linear combination (WLC) method and are representative physical suitability and Euclidean distance of each parcel from the closest point of path network is utilized to measure accessibility factor. Spatial externalities of neighborhood land uses are important component in simulating land use changes and using cellular automata is justifiable if neighborhood rules, as heart of the cellular automata models, are defined correctly. One of the limitations of neighborhood rules is the lack of theoretical foundation and empirical validation. So, CA urban models are technology driven rather than theory, due to considering hypothetical ideas about urban dynamics. Therefore, neighborhood rules are often defined by trial and error. In this study, the neighborhood effect is defined based on a function of three components including compactness, dependency and compatibility and the way of land use interactions is studied in three service levels of local, district and regional. Compactness is the tendency toward creating a kind of land-use adjacent to another similar one; dependency is depending one land-use on the other land-uses for supplying its needs; and compatibility is defined as the arrangement of two or more land-uses together without any significant negative effects. The neighborhood effect of each parcel for each land-use is calculated from total effects of all adjacent parcels in the form of three classes of compactness, dependency and compatibility. It should be considered that each of factors has various impacts in land-use interactions and can be combined together with different weights, regarding the subject land-use. Therefore, relative importance of three factors is defined using expert knowledge captured based on AHP method and then the neighborhood effect of each parcel is computed using to WLC method.Results and This model is implemented and assessed using data of Tehran 22 municipal region, in two scenarios: land use preference and maximum suitability. The results are illustrated high ability of the model in simulating land use interactions and presenting urban growth in an acceptable level of consistency among the urban land uses. Evaluating the results in the case study area shows an average score 0.77 and 0.55 in Scenario 1 and 0.75 and 0.47 in scenario 2 (from 0 to 1) for compatibility and dependency of new allocated parcels, respectively. The proposed model can be utilized by urban planners to assess different urban scenarios for better decision making and urban planning.

Modern cities require an optimum management of various spatial elements such as electrical distribution network, road network, urban servicing centers, and public facilities. Spatial information usually is stored in a geodatabase for a better data accessing, removing, adding, and transferring. Capital cities, generally, are experiencing many changes with respect to construction and land use. Tourism services quality, location and characteristics may change in such cities too. An example of such changes can be observed in restaurants. Thus, updating such geodatabases is very necessary, to gain tourists trusts in using a tourism application services (e.g. an application about finding desire restaurants). Traditional or authorized data acquisition’s methods consume huge amount of time and money, while Volunteered Geographic Information (VGI) are almost free of such limitations. Therefore, VGI has been considered as an alternative solution for some applications which do not require much money or have to be updated during a real time process. VGI has also been widely used when application developers expect automatic performance of a system. The aim of this paper is to put the capability of automatic updating to a geodatabase of restaurants information by Volunteered Geographic Information in practice. To do so, a mobile application has been built to collect volunteers’ data, but all input information should be confirmed for data qualification. Thus, the application should filter VGI data based on their quality. Therefore, VGI quality should be assessed first, and then the data with acceptable quality should be chosen and used in geodatabases. VGI quality inherits spatial data quality characteristics. Hence, it is multidimensional and includes various elements (e.g. positional accuracy, thematic accuracy, temporal accuracy, completeness, logical consistency and etc.) which are different in every application. In this paper positional accuracy, temporal accuracy, thematic accuracy and credibility are considered as effective elements of VGI quality. These elements should be integrated, because the geodatabase require an explicit parameter so as to decide which data deserves to join others. In such cases, Multi Criteria Decision Making (MCDM) methods can be useful for deciding how to integrate criteria to get realistic results. As experts from various fields evaluate the quality of elements, recognizing most important criteria is hard and it is arduous to specify weighs for each element. So, it is required to employ a method that decreases the effect of weighting criteria in MCDM operation. In this paper, among MCDM methods, Ordered Weighted Averaging fuzzy quantifier guided operator is selected to merge VGI quality elements and assess final quality. The proposed method tests various scenarios and help decision makers to decrease weighting effect. After testing the mobile application, final results demonstrated that the application is successful in completing information of 60% of restaurants. As a conclusion of the research, it is cleared that high thematic VGI quality will prevent errors in populated restaurants area.

Most of common classification algorithms in remote sensing are based on spectral characteristics of pixels. These approaches ignore the spatial information of data, such as texture, in classification process. Simultaneous usage of texture and spectral information is a new trend in remote sensing image classification which has been considered in this study. We have evaluated the efficiency of gray-level co-occurrence matrix texture features (GLCM) extracted from panchromatic (PAN) image of ALI detector in improving the classification accuracy of Hyperion hyperspectral (HS) data in urban regions of Tehran. Classification is performed using a support vector machine (SVM) classifier with a Gaussian kernel. In our experiments, we have considered three different cases: a) classifying original Hyperion data, b) classifying Hyperion data pansharpened by color normalized transform (CNT), and finally, c) simultaneous use of GLCM texture features of panchromatic data and the spectral features of pansharpened data in classification process.In case b and c, for pansharpening HS data we have performed the following steps:1.Registering HS data with PAN data using ◦nineteen ground control points,◦polynomial warping of second order, and◦Nearest neighbor interpolation.1.Selecting a subset of HS bands which spectrally overlap with PAN image.1.Fusing the spatial information of PAN image into the HS subset bands, obtained in step 2, using CNT method.Moreover, as GLCM features, we have extracted 8 texture features from GLCM matrices: mean, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment, and correlation. In order to assess the influence of the size of GLCM extraction window on the quality of texture features, we have considered various window sizes: 3×3, 5×5, 7×7, and 9×9.At the first phase of our experiments, we compared classification results obtained using the original HS data with the results obtained from the pansharpened HS subset – see Table 1. The results showed an increase of about 15% in the average classification accuracy when using the pansharpened data. At the second phase, we combined each of the texture features individually with the pansharpened HS subset. The results are given in Table 2. As the table suggests, regardless of the type of texture feature and the size of the GLCM extraction window, the combinations improve the overall classification accuracy (OA) of data. However, texture features show better quality when extracted from GLCM matrices obtained using 9×9 neighborhood windows. In addition, we observe that regardless of the size of GLCM window, dissimilarity feature delivers the best results.To summarize, by using the pansharpened HS subset instead of the original HS data, we achieved about 15% gain in the classification accuracy. Moreover, combining dissimilarity texture features –extracted from GLCM matrices obtained using 9×9 neighborhood windows– with the pansharpened HS subset improved classification results. In our experiments, we achieved about 5% increase in OA compared to that of using pansharpened HS subset alone.

The increasing number of satellite sensors, with various characteristics in terms of spatial and spectral resolution, motivates the developments of automatic methods for better exploitation of their huge available information content. In this regards high resolution satellite images, which provide very detailed and accurate information of the urban areas, have attracted more research attentions. As an early step, geo-referencing of satellite images are essential by which image position observations can be transformed to the ground position information. This process needs some accurate control points which are traditionally provided by human operators. Considering the time-consuming and tedious task of control point provision automatic feature extraction from satellite images, especially distinct point features, has been the subject of many researches in the last decades. In this paper a novel method is proposed for accurate automatic extraction of road intersection points in candidate regions. These point features, if accurately extracted, are applicable for image geo-referencing or image-to-image registration. Roads are presented as wide ribbons in high resolution satellite images which cause a considerable uncertainty about the exact position of their intersection points. This uncertainty, which makes road intersection extraction a difficult task for human operators, motivates the aim of this research to develop an automatic and accurate extraction method. In the first step of the proposed method the candidate intersection region is divided to three clusters via a simple K-means clustering. The largest cluster is than labeled to background and omitted from the rest of processing. In continue angular texture information, extracted from a rectangular element, is used to detect road pixels. Road edge pixels are then found via the famous canny edge detector. Among the detected road edge pixels those which are close to the junction are found using Circle Centered Edge analysis. In the last step, a novel vectorization scheme is proposed to find out the exact position of road junctions. This scheme, developed based on the proximity and parallelism concepts, includes the following sub-steps: 1) line fitting to edge pixels; here a line is fitted to the fist ten pixel and then the inclusion hypothesis of other pixels is verified by the evaluation of their distance to the fitted line, 2) eliminating the lines which are not related to road and determination of parallel lines, 3) grouping and merging of the same-type lines, 4) determining the co-side lines (i.e. road lines which are at the same side of a road segment) and 5) road centerline determination followed by positioning of the road intersection point. Given the road junction area, where at least 3 road segments intersect, the focus of this research is only on the exact and automatic positioning of the intersection point. The proposed method was implemented on four IKONOS sub-images taken from an urban area, Shiraz Iran, where in all cases the 0.5 accuracy proved the efficiency of the method. This method is also rotation invariant which means its applicability for all road segment orientations. Being resistant against the presence of vehicles and also the capability to extract more than one intersection points in a candidate region are other advantages of the proposed method.

In this paper, an approach has been proposed in order to reconstruct the flat buildings using the LiDAR data and the digital aerial images; because on one hand, these types of buildings constitute the main structure of IRAN mega cities. On the other hand, separating the roof planes and reconstructing them is a challenging matter due to the fact that the normal vectors of the building roof planes are completely the same and there is not any intersection among them. In this regard, firstly, 16 potentially primary features were produced and the optimum features were extracted using the genetic algorithm and the KNN algorithm to detect the buildings. Subsequently, an approach was presented to eliminate the misclassified regions and to improve the detection results. In the designed reconstruction approach, each building parcel was considered separately in order to reduce data redundancy and increase the result accuracy. After selecting the considered parcel, an initial class for building roof planes was achieved by employing the surface slope differential. Actually, a threshold was specified and the regions with the slope differential value less than it were removed. Therefore, an initial class of primary planes were recognized and labeled by connected component algorithm. The roof planes were improved and detected completely by textural and altitudinal analyzing. The acceptable range was determined by computing the median minus the variance of the elevation in the plane to the median plus the variance. The median was selected as a criterion, because it is not sensitive to the noise of data and it causes to choose a reliable value. To identify the adjacent planes, the recognized plane was scanned row by row and column by column. In each row/column, the pixels with values more than zeros were extracted and analyzed. If there exists a variation, the pixels numbers were extracted and considered as the adjacent. Afterwards, the boundary nodes of each plane were extracted using the chain code algorithm. The optimum nodes should be selected as boundary and the planes should be placed beside each other without any intersection and gap. Hence, by investigating the distance and angle, very close nodes were removed and replaced by their mean. Also, the nodes that cause creating the intersection or gap were recognized and rectified in order to eliminate this error. Then by searching around of the extracted nodes, the corresponding terrain node for each boundary node was obtained. The equation of each plane was computed by the coordinates of the inner nodes in that plane. Finally, the equation of planes, extracted boundaries, and nodes of the floor were utilized for reconstructing the final model. The proposed approach was implemented on some building blocks with different structures and the accuracy of the reconstructed plane was evaluated in both altimetric and planimetric criteria. The evaluated results were shown 84.56% accuracy on average for planimetric reconstruction, 0.212 meter root mean squared error for planimetric corner coordinate, and 0.145 meter root mean squared error for altimetric reconstruction. These results clarify the good performance of the proposed approach for reconstructing buildings with flat roofs.

A number of classification algorithms have been proposed for PolSAR data. There are basically three approaches: (1) algorithms based on a statistical model, (2) algorithms based on the scattering mechanism of electromagnetic waves, and (3) knowledge-based algorithms. In the first category, classification is done based on the specified probability density function. The second approaches, the classification of PolSAR images are based on some form of target decomposition theories. Third approaches included two steps. First, extraction of knowledge from PolSAR data, and second application of this knowledge to the classification of other pixels. In these approaches it is possible to include scattering model results and common knowledge about the targets.The main purpose of the proposed method is the knowledge-based and object-based classification of PolSAR data. To improve the classification results, the contextual information should be considered for incorporation into the classifiers. For this, the object-oriented package eCognition was used to implement the object-oriented image analysis of PolSAR images. The multi-resolution segmentation module was used to perform object delineation in this study. Proposed method can apply prior knowledge, expert knowledge and data knowledge in the process of classification. A combination of Support Vector Machine and Decision Tree (SVM-DT) was presented for fusion of three level knowledge. The SVM based binary decision tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The SVM-DT architecture was designed to provide superior multi-class classification performance. Utilizing this architecture, N-1 SVMs needed to be trained for an N class problem, this can lead to a dramatic improvement in recognition speed when addressing problems with big number of classes. Since DTs are often constructed using a portion of the training patterns to accomplish individual classifications at each node, the node classifiers should be robust in the presence of “bad” samples or outliers. The SVM’s remarkable performance with regard to sparse and noisy data makes them suitable for binary classification trees.The incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. In this paper the prior knowledge was used for compensating the unbalanced data in SVM classification. Furthermore, the expert knowledge provides information for designing a decision tree and a feature selection for classification algorithm. Also, the data knowledge was used in various classification steps e.g. features for SVM classification and feature selection. A Radarsat-2 image of the Petawawa forest area including six land cover classes: red oak (Or), white pine (Pw), black spruce (Sb), urban (Ur), water (Wa), and ground vegetation (GV) was chosen for this study. In this research, six experiments were provided for evaluating three level knowledge effect: Wishart, SVM, SVM-DT, object-based SVM-DT, object-based SVM-DT and feature selection, adding prior knowledge to object-based SVM-DT and feature selection. The results show the positive effect to forest classes for adding various knowledge to the classification. Although, the effect of some knowledge on other classes is non-positive. Eventually, the overall accuracy of the proposed method is 87.36. The proposed algorithm outperformed the Wishart classifier by 15% and SVM classifier by 9%.

As adaptive neuro-fuzzy inference system (ANFIS) has shown a high capability in solving various complicated problems, its usage has been increased so far. LiDAR is an active sensor which operates based on measuring distance by sending pulses to the ground and receiving the backscatters. This technology gives the 3D position of a point directly. Because of using millimeter level laser ranging accuracy, LiDAR is highly accurate. Dense point clouds of LiDAR can be directly used in simple applications, but the full manipulation of the LiDAR potentials and capabilities needs new methods and researches that differ from those in traditional Photogrammetry. The main output data of LiDAR are point clouds. Each point has two range and two intensity values for both the first and the last pulses. In some areas where there are some trees, the values for the first and the last pulses may differ, in which the first pulse data includes upper surfaces of trees, whereas the last pulse data includes lower surfaces, mainly ground. ANFIS is able to deal with large amounts of data with linear or nonlinear relations. In our study, the combination of digital aerial images and LiDAR data were used for the first time to probe the capabilities of the ANFIS as a classifier. The fact of non-linearity and ambiguity of this combination makes this challenge so hard. The main goal of this research is to detect buildings in city scenes from digital aerial images and LiDAR data using the ANFIS. In this regard, a genetic algorithm is run for feature selection. Four features were selected by genetic algorithm. These features were generated as ANFIS inputs including Green band, normalized difference vegetation index (NDVI), and normalized digital surface model (nDSM) using two different algorithms via morphological operations. The proposed ANFIS used three different algorithms to build its fuzzy inference system structure including grid partition, subtractive clustering, and fuzzy c-means clustering. Also, as there are many methods in building and tree detection as mentioned before, the main question is which of them is better among the others? This is not an easy question to answer because these methods are not evaluated over a unique data-set. To overcome this problem, fourth working group of third commission (WG III/4) in the international society of photogrammetry and remote sensing (ISPRS) has provided some benchmark data-sets, and has encouraged all researchers around the world to evaluate their methods on these data-sets. The results were evaluated on three different test areas, known as Areas 1, 2, and 3. The achieved results were compared with each other, as well as with ISPRS WG III/4 participants’ results, by considering Completeness, Correctness, Quality, and RMS indices per-area and per-object levels. The achieved results demonstrated the capability of the proposed ANFIS in detecting buildings in complex city scenes in comparison with other methods. Although there are some typical errors among participants’ results, most of these errors have resolved in ANFIS-base approaches. Proposed ANFIS-based methods achieved Completeness of 100% in all three test areas for buildings larger than 50 m2.

Satellite image classification is considered as one of the most common approach for information extraction from remotely sensed data. With the advent of microwave sensors and taking into account the advantage of distinctive characteristics of the microwave range in electromagnetic spectrum extraction of different information in comparison to optical sensors are provided. Polarimetric information has significant implications for identifying different phenomena and distinguishing between them. In synthetic aperture radar imagery unlike hyperspectral imagery, where in spectral bands provide required features for pattern recognition process, we need to construct such features. Nowadays we can extract a wide range of features from polarimetric images using target decomposition theorem and SAR descriptors. In this paper at the first stage we try to extract features in three categories including original data features, decomposition features and SAR parameters. Then SVM algorithm with RBF kernel is used to classify polarimetric image. Due to the binary nature of support vector machines algorithm, the one against all approach is used to perform a multi-class classification. In this approach for m class m binary classifier are considered. In this study the genetic algorithm is used in order to calculate kernel parameters, feature space dimension reduction and selection of optimal features. In this study the superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset on Polarimetric imagery are demonstrated. The other point of the paper is higher accuracy of SVM classifier by kernel parameter selection using genetic algorithm and considering all the features in relation to optimal feature selection using genetic algorithm and kernel parameter selection using grid search. In another section of this study object based image analysis is used to compare the performance of SVM classifier in conjunction with genetic algorithm with OBIA. In OBIA approach, at the first stage, an image to be analyzed is segmented into individual image objects in an object based approach. The image pixels from the image are grouped to form the objects in a segmentation process. The created image objects should represent the objects in reality. In this research, multiresolution segmentation algorithm was used to create the image objects. By delineating objects from images, object based image analysis enables the acquisition of a variety of additional textural and spatial features, which are helpful in improving the accuracy of polarimetric image classification and also reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects. To extract optimal features, it is essential to use an appropriate analysis tool. For this purpose, in this paper SEaTH analysis tool was used. In this method, by using Jeffries-Matusita’s measure, the features are extracted as the optimal features in an appropriate separation of the probability distribution function for the training samples belonging to different classes. The proposed method was applied to the RADARSAT-2 imagery of an urban area in fine quad polarimetric mode. This imagery was selected in order to include a variety of land cover categories i.e. urban, water, bare soil and vegetation. The results demonstrated that the accuracy of OBIA approach is higher than support vector classification using grid search and all input features. Finally, we demonstrate that classification accuracies are significantly higher by simultaneously optimization of SVMs parameters and input feature subset on Polarimetric imagery.

In urban space, the need to different facilities and diverse land uses increases continuously. Continuous changes in the demands of the citizens results in rapid and frequent land use changes. Therefore, dynamic characteristics of urban environment should be considered in urban planning. On the other hand, land uses have different effects on each other. In other words, any change in the land use of a parcel or zone, will results in tendency of its neighboring parcels for land use change. Therefore, proposing of new land use arrangements after any occurred land use change could be a proper response to such tendencies. The main goal of this study is to propose a method, based on GIS and NSGAII optimization algorithm, for generating optimum land use arrangements after any occurred land use change.Usually, the output of a multi-objective optimization algorithm is a collection of optimum solutions. Selection of appropriate solution from such a collection needs extra efforts and processes. Therefore, another goal of this research is to use an appropriate clustering method that helps the user to select the most preferred solution. With such a method, the decision maker can introduce his planning priorities, perceive the resulted scenario and select accordingly. In this research, ant colony clustering algorithm is used for clustering, first because of its high speed, and then because the representative of the clusters are selected from the Pareto front solutions. In this research, for modelling of the aspects of land use change and its factors, four objective functions are considered, which are: the maximization of land use compatibility, the maximization of land use dependency, the maximization of land use suitability, and the maximization of land use compactness. Finally, the providence of per capita for different land uses is considered as constraints. The ant colony clustering algorithm is used for clustering of the found solutions (land use arrangements). The developed method is implemented and tested using the data related to some districts of region 7 of Tehran.Different evaluations are considered and carried out for the results of optimization. These include the convergence trend, repeatability test, and the comparison of the previous land use arrangement with the optimized ones. In the resulted optimized land use arrangements, the levels of objective functions are much better than the previous arrangement. Furthermore, the required per capita for different land uses are much better satisfied. The highest improvement in the objective functions is 36%, which is related to land suitability. In addition, the required per capita is improved by 18.5% of. The results of clustering using ant colony clustering algorithm are compared with those of K-means and Fuzzy K-means. The comparison showed that the ant colony clustering algorithm is faster. In addition, the results of this clustering method are exactly the original solutions of the land use arrangement optimization.Finally, the developed method can help urban planners and decision makers to correct and change the detailed urban plans according to any occurred land use change. One of the limitations of the detailed urban land uses plans is that they are not flexible and cannot opt to the deviations from the plan. This research is one step in the development of a general approach to dynamic urban planning. Such a planning approach can respond to the continuous and dynamic changes of the land uses in urban space.

The growing trend of urbanization has led to an unsustainable environment that become one of the main concerns of today’s city managers. Nowadays, the role of transportation in sustainable urban development is very clear and undeniable. On the other hand, the need for sustainable transportation reinforces the idea of using non-motorized transportation. Non-motorized transport is a form of entertainment and recreation, and can provide the possibility of cheaper trips. The walkability index is one of the important issues in the non-motorized transportation. The determination of walkability index leads to the recognition of environment as a factor affecting the extent of encouragement of individuals to walk and ride bicycle. As a result, one can improve the existing conditions so as to enhance the physical activity level of individuals in a society. The existing methods of determining the walkability can be summarized into three types: 1- objective; 2-subjective, and 3-experts’ field studies. The objective methods determine the extent of walkability index through GIS data. The subjective methods measure the walkability index with the help of public opinions (i.e. survey). The field-study methods collect the experts’ viewpoints and comments so as to measure walkability index. The objective methods usually use certain parameters such as population density, dwelling density, land-use diversity, access to stores and urban services, connectivity, intersection density and network density. All across the urban planning community, much effort is currently being put into providing safe and friendly environments that encourage walking in cities. The main goal of this study is to develop a simple yet efficient measure that captures properties of pedestrian friendliness, which is region-specific, and that can be particularly useful to urban planners and metropolitan planning organizations. In this research, the development of the walkability index in the city of Qom has been introduced. Parameters affecting the walkability index were identified and assessed. These parameters include: Land-use diversity, population density, intersection density, network density, access to public transportation, access to religious sites, access to primary schools and kindergartens, access to secondary to pre-university schools and parking sites. A noteworthy point considered in all of the parameters is the normalization of results (i.e., keeping its values between 0 and 1). Then, the ranking of the alternatives was produced using AHP-TOPSIS method in five age/sex classes. The target population of this research is people who are willing to walk for daily tasks and recreation. The result of this study showed that the districts of Sarehoz, Razaviye, Labechal, Nobahar, Pamenar, Chehel Akhtaran, Sarbakhsh, Niroghah, Zad, Zandian, Emam hosein town, Vadiassalam, Darvaze Rei and Ali Abad Sadegan have the best condition among other districts. Also, the districts such as karbaschi garden, Berasun, Yazdanshahr, Shah seiied ali, Fatemiye town, Noghatar, Kasegarha, Panzdah khordad and Bajak two have the worst condition. Generally, the central districts have better conditions than the others. Final evaluation of results by five Likert questionnaire (strong, good, moderate, weak, and poor) were done. Finally, the result showed that the access to parking has the weakest point and the land-use diversity has the highest effect on the proposed walkability index.

Land Surface Emissivity (LSE) is a significant parameter in many different land surface studies. It can be used as an index for analyzing the structure of the material. Furthermore, LSE estimation is a significant factor in the land surface temperature estimation from remotely sensed data. In this study we presented a novel operational algorithm for retrieving LSE from Landsat-8 thermal bands (i.e.: band 10 and 11) based on vegetation index (VI). The study includes three steps: I) building up simulated dataset for Landsat-8 bands II) threshold determination for VIs and correlation analysis between VIs and LSE III) derivation regression between LSE and Vis. First, the simulated dataset has been built up based on spectral library and spectral response function of Landsat-8. ASTER Spectral Library (ASL, http://speclib.jpl.nasa.gov) and Vegetation Spectral Library (VSL), which is published by system ecology laboratory at the University of Texas at EL Paso in cooperation with the colleagues in University of Alberta (http://spectrallibrary.utep.edu/SL_browseData), were used to build up simulated dataset. These Library contain directional hemispherical reflectance of the different type’s area. Then the threshold has been determined for each VIs and correlation analysis has been done between each VIs and LSE. The correlation between the vegetation indices and emissivity values was analyzed. The vegetation indices that were tested include: the Simple Ratio, SR, the Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, Transformed Vegetation Index, TVI, Soil-Adjusted Vegetation Index, SAVI, Leaf Area Index, LAI and the Modified Soil-Adjusted Vegetation Index, MSAVI. The results of this analysis show that the correlation between VIs and emissivity is acceptable; therefore these indices are used for retrieving LSE. The results show that the maximum correlation occurred between NDVI and Emissivity, also the minimum occurred for MSAVI. For determining the threshold in this study we assumed that the area can be separated into three categories, including bare soil area, vegetated area and partially vegetated (mixed area). Then the statistical parameters (max, min, mean and standard deviation) for each category (bare soil, vegetation and mixed area) were calculated and based on these parameters, threshold values were determined for each category. Finally, regression relations have been derived to estimate LSE based on VIs. Support Vector Regression, SVR, and least square method were used for this regression. The RMSE of regression is different for each VIs. However, this value is less than 0.0035 for all VIs. The minimum of them occurred for NDVI and TVI also the maximum is for MSAVI. The presented method was evaluated by using an independent dataset. The result shows that the RMSE of LSE for band 10 and 11 is less than 0.007 and 0.009 respectively. The presented method is robust for estimating LSE from Landsat-8 satellite imagery and also is simple and do not need any auxiliary data. For further study, local comprehensive dataset can be built up and also the effect of atmospheric parameters or dust on regression coefficients can be analyzed.

The existence of speckle in Radar images is an inevitable occurrence. The speckle noise is a granular disturbance that models often as a multiplicative noise in single-channel SAR images. This noise that dependent on signal, introduces for variations of phase of returned signals that appears as point-point pattern. Being speckle, make complex more the explanation and analysis of images and also decrease the access to the image data, so it is important which appropriate speckle-reduction algorithm should be choosing. therfore, a section is allocated to the introduction of speckle noise model. It provides some important facts about how the speckle formed and explains which probably density function is followed by the amplitude and the intensity image. Several adaptive filtering methods have been discussed to deal with issue in this paper such as: Mean, Kuan, Frost, Lee, enhanced Lee and Gamma-MAP. Then according to statistic characteristics of speckles and texture characteristics of SAR images an adaptive speckle-reduction algorithm with size-changeable window based on relative standard deviation have been put forward. This new algorithm uses a moving window like other typical filters, but its window is divided to four smaller windows that every of them is called subwindow. The relative standard deviation of every subwindow is used as compare factor. If all subwindows are in homogenous region the mean filter is applied on whole initial window. And if any subwindow has the edge or high-frequency information, it must be omitted from proceeding process. The proposed filter is size-changeable because, if all subwindows are not accepted by the rules, the filter need to reduce the size of initial window to provide a region for filtering process.This paper benefits some worthy indices to demonstrate the ability of proposed filter against common filters such as equivalent number of looks (ENL), speckle suppression and mean preservation index (SMPI), edge save index (ESI), mean square error (MSE) and signal to noise ratio (SNR). To analyze the proposal algorithm and other common filters, used a simulated four-look SAR image and two real SAR images i.e. Flevoland dataset from AirSAR airborne SAR sensor and Oberpfaffenhofen dataset from ESAR airborne SAR sensor, respectively. The simulated SAR image is determined according to multiplicative model and gamma distribution. It is used to show a primary evaluation of proposed filter and other filters. In the first step, the results are satisfying. For example, the indices of standard deviation (SD) and ELN for the proposed filter are 39.53 and 192.59 that in comparison to other filters are gratifying and agreeable. In the next step, the all filters that are described in this paper are applied on Flevoland dataset. As the experience results show, the proposal algorithm has a satisfying performance in removing speckle noise along with very good saving edge characteristics, targets scene and mean of image toward usual speckle-reduction filters. For instance, the indices of ENL and SNR for area 1 of first image are 17.22 and 7.91, respectively, that are highest values between other common filters. To survey more precisely, the mean and the enhanced Lee filters are selected to compare to the proposed filter by second real dataset in the ways of ability to remove speckle, preserve edge and maintain point target.

Finding the shortest path from origin point to destination point is of vital importance in different cases. In a network, the length of arcs could show the length of the path, time of the path, or any other parameter. A fuzzy shortest path has a variety of applications. Now suppose that there are arcs with no specified length, or with specified length that vary depending on other parameters such as traffic, accidents etc. Moreover, on certain occasions such as smuggling, security forces may doubt the weight of arcs. In such cases, the use of fuzzy shortest path would not be efficient. The Intuitionistic fuzzy set theory can be considered as a generalization of fuzzy set theory in which non-membership function is used in addition to membership function, independently. Note that in fuzzy theory, no difference is considered between presence of data or reasons in favor or against any given subject. In other words, if membership function of an element be half from the fuzzy set, we cannot infer that information was little or that negative and positive reasons were provided with the same amount. Whereas the Intuitionistic fuzzy set and logic is capable of overcoming a number of the limitations of the fuzzy algorithm theory such as supporting doubts and uncertainty. On the other hand, due to the fact that one of the present issues in the graph is finding the shortest path in terms of uncertainty and lack of adequate information of distances. In this paper, the shortest path of Dijkstra algorithm is expanded for the graph with Intuitionistic fuzzy arcs having incomplete data. In this article, two problems with corresponding solutions are presented. The first challenge is about combining the arcs solved by using triangular Intuitionistic fuzzy numbers. The second problem concerns the method of comparing the arcs. To compare the arcs, there are numerous ways including utilizing centroid, maximum and minimum sets, integral values etc. Finally, integral values method was implemented. The reason for using this method is capability to differ between state of decision-maker like optimism and pessimism. So one can change equal inputs accordance to different condition to give different outputs. In this regards, we provide a numerical example of a road network. This network includes 25 nodes and 46 arcs. It is assumed that the value of arcs is triangular Intuitionistic fuzzy numbers as noted above. Then, the algorithm was tested on the network and was compared with the conventional fuzzy method. Finally, the result of algorithm has been compared with the figures and tables and presented difference of the fuzzy and intuitionistic fuzzy paths. It should also be noted that in the case of information lack and algebraic uncertainties abound, Intuitionistic fuzzy logic will be useful, bearing more appropriate results compared to cases done with fuzzy logic. That is because the use of this algorithm allows us to analyse the possible routes pessimistically, cautiously, optimistically and moderately. Hence, information and lack of information as well as doubts and uncertainty will also be taken into account. As a result, the use of this algorithm provides results that are more adaptable to the given condition to be implemented by the decision maker.

LST and LSE are two significant parameters in climatic, hydrologic, ecological, biogeochemical, and related studies. LST is an important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control index for climate models. Emissivity, is an indicator of land-cover type and resources, and also a necessary element in the calculation of LST from remotely sensed data. The main purpose of this paper is to present an operational algorithm to retrieve the Land Surface Temperature (LST) and Land Surface Emissivity (LSE) from Landsat-8 satellite images. The proposed algorithm is Split Window (SW) with band 10 (10.6 – 11.19 μm) and band 11 (11.50 – 12.51 μm) of Landsat-8 in thermal infrared range. Also for LSE mapping, the Normalized Difference Vegetation Index (NDVI) method has been suggested. This study contains two main steps: first, emissivity values of bands 10 and 11 are calculated and NDVI threshold values have been determined to separate the bare soil, fully vegetated and mixed areas from each other. Then, by using a regression relation, the values of the emissivity of the bare soil samples and mixed area have been derived. A constant value of emissivity is also used for the fully vegetated area. For a regression relation and a constant value in this study, reflectance of Landsat-8 bands has been simulated based on using two different spectral library data and relative spectral response function of Landsat-8 thermal wavelengths. ASTER Spectral Library (ASL, http://speclib.jpl.nasa.gov) and Vegetation Spectral Library (VSL), which is published by system ecology laboratory at the University of Texas at EL Paso in cooperation with the colleagues in University of Alberta (http://spectrallibrary.utep.edu/SL_browseData), were used to create simulated dataset. For validation of this step according to the lack of accurate methods for retrieving LSE from Landsat-8 imagery, the method can’t be validated with real data. Therefore, the test simulated data, which are selected randomly from simulated data, were used for validating the method. In the second step, three simulated datasets have been used. One of them for obtaining the SW coefficients and others for validating the proposed SW algorithms. The simulated datasets should include brightness temperatures, surface temperatures, emissivity and atmospheric parameters (atmospheric transmission, upwelling and downwelling radiance) for the TIRS bands. For this purpose, for each Landsat-8 TIRS band (i.e.: band10 and band11) brightness temperatures are obtained from the RTE by inversion of the Planck’s law. Surface temperatures were chosen based on the temperature of the first layer of the atmospheric profiles (T0) as T0 − 5°K, T0, T0 + 5°K, T0 + 10°K, and T0 + 20°K. The emissivity was extracted from spectral library and the atmospheric parameters have been simulated using the MODTRAN for the standard atmospheric profiles of MODTRAN (including: tropical (TRO), mid-latitude summer (MLS), mid-latitude winter (MLW), sub-arctic summer (SAS), U.S standard (USS), sub-arctic winter (SAW)). The SW algorithm coefficients for Landsat-8 were calibrated. SW algorithm coefficients were retrieved by using the least square approach based on the simulated data. Finally, this SW algorithm was tested with three datasets: simulated data, real data and satellite data. Results show that the RMSE value retrieved from the SW algorithm is equal to 1.21°k, 1.76°k and 1.03°k respectively for the three datasets. Therefore the results indicate that the proposed SW algorithm can be a suitable and robust method to retrieve the LST map from Landsat-8 satellite data.

Unmanned aerial systems (UAS) are one of the latest technologies utilized in the hazard management and remote sensing. Nowadays, tendency in the development of UAS is toward autonomous navigation or hybrid tasks. In this context, development of comprehensive, efficient methodologies for path planning, control and navigation of UAS can be regarded as one of the fundamental steps for the development of autonomous systems. Up to now, different planning algorithms have been proposed in the specialized literature in order to enrich the framework of autonomous navigation of unmanned aerial systems. However, few efforts have been devoted to design new chaotic path planners for determining the optimal trajectories of these aerial systems in urban areas. An effective path planning technique can attain mission aims with respect to various restrictions of the UAS and less computational time.Chaos theory is one of the most studied theories with different applications in engineering and technology. Most of the natural processes demonstrate chaotic behavior such as black hole and clouds. Past researchers showed that if an evolutionary algorithm be hybridized with chaos, its performance will have improved, considerably. However, most of the evolutionary algorithms are inspired from nature, but all of their steps are random based motions. But nature is not either completely random based or chaotic. Hence, the combination of these theories should be more realistic. With this regard, evolution and chaos are related to each other narrowly in most of the complex natural systems. It is evidenced that some of the chaotic signals can alleviate the premature convergence problem of the evolutionary algorithms in tackling optimization problems.In this article, first, UAS path planning is modeled as a 3D constrained optimization problem. In this modeling, the aim is the optimization of path, fuel and safety with respect to different restrictions. After scheming and suggesting of general planning framework, UAS path planning problem is investigated by comparative study with regard to the studied scenario. For this aim, evolutionary planner is implemented in order to minimize the flight height, path length and energy consumption considering different restrictions such as safe altitude, turning angle, climbing slope, gliding slope, no fly zones and mission map limits. Then, a comprehensive model is employed to describe route-planning task, and then, based on the hybridization of chaos theory with evolutionary computing, four new evolutionary optimizers are developed. Hence, this paper developed four chaotic optimizers including particle swarm optimization, differential evolution, imperialist competitive algorithm and artificial bee colony technique based on 14 chaotic signals.In the rest of this paper, analyses, and extensive performance evaluation of the designed trajectory-planning approaches are performed according to the success rate results, precision and quality of the results, CPU running times, and convergence speed. The results show that the proposed framework can be utilized in represented scenario as an effective path planner. Proposed strategies are capable to compute the optimal paths more efficiently in comparison with the standard algorithms. From the results it is known that the chaotic differential evolution with logistic map can outperform the other compared algorithms.

Rapidly discovering, sharing, integrating and applying geospatial information are key issues in the domain of emergency response and disaster management. Due to the distributed nature of data and processing resources in disaster management, utilizing a Service Oriented Architecture (SOA) to take advantages of workflow of services provides an efficient, flexible and reliable implementation to encounter different hazardous situation. The implementation specification of the Web Processing Service (WPS) has guided geospatial data processing in SOA to become a widely accepted solution for processing remotely sensed data on the web. This standard is a generic interface without any restriction on supporting any specific process. It is possible to integrate different geoprocessing services in an individual WPS service and expose it as a single web service. This paper presents an architecture design based on OGC web services for automated workflow of acquisition, processing remotely sensed data, detecting fire and sending notifications to the authorities. A basic architecture and its building blocks are represented using web-based processing of remote sensing imageries utilizing MODIS data. As a framework for this application, we consider a generic architecture, which consists of several components. These components are divided into layers according to the types of functionality they provide. A core module is provided to manage the interactions between different layers and components. To achieve the interoperability issue, OGC standard interfaces are utilized for storing, processing and representing remote-sensing data. A workflow of distributed processes are used for hot pixel detection using MODIS data to implement the feasibility of the proposed architecture. A composition of WPS processes, in a centralized opaque pattern, is proposed as a WPS service to extract fire events. The workflow includes several processes such as pre-processing of data, radiance number calculation, brightness temperature process, fire event rules computations. The paper highlights the role of WPS as a middleware interface in the domain of geospatial web service technology that can be used to invoke a large variety of geoprocessing operations and chaining of other web services as an engine of composition. The usage of available data and processing resources on a flexible infrastructure as a composite service is one of the benefits of service oriented architecture that provides a higher level of functionalities and applications. The applicability of proposed architecture by a real world fire event detection and notification use case is evaluated. For the implementation of described system, open source web service software and java technology have been used. OGC standard interfaces of data access services, Web Coverage Service (WCS) and Web Feature Service (WFS), have been used to store and retrieve required data in a standard way. To notify the realized events of fire hazards to the authorities the standard implementation specification of OGC, Web Notification Service (WNS), was used to provide alerts in different protocols of communication. A GeoPortal client was developed to manage data, metadata, processes, and authorities alongside the mapping of results of fire event detection processes on satellite imageries. Investigating feasibility and benefits of proposed framework exposes that this framework could be extended and reuse the components of this architecture for wide area of geospatial applications specially disaster management and environmental monitoring.

In traditional hydrographic surveys, chart datum is usually determined using spectral analysis of coastal tide gauge observations based on the lowest water level. Due to the variation of tidal amplitude and phase components in different locations, such chart datum is only valid in coastal areas around the tide gauge stations; it is then hardly accurate when a few tens of kilometers away from the tide gauge stations in the off-shore areas. This study models the separations between the elliptical reference WGS84 and the chart datum using the data collected by the satellites Topex/Poseidon and Jason-1 for the periods of 1992-2002 and 2002-2008 in Persian Gulf and the coastal tide gauge data of the Oman Sea. The major advantages of the technique used in this study are highlighted as follows. The satellite altimetry observations on the shores and also in the shallow water is not of good accuracy and precision. An appropriate solution to this problem is to use coastal tide gauge observations near the time series generated from the satellite observations. On the other hand, chart datum determination based on the coastal tide gauge observations is suitable only for the areas close to the coastal tide gauge stations. However, due to the amplitude and phase variations of the tidal components in different parts of the sea, the accuracy of such a chart datum is not appropriate for the areas that are far away from the tide gauge stations. In this study, the satellite altimetry observations are combined with the data obtained from the coastal tide gauge stations. Due to the existence of point-wise time series in the study area and also creation of the “quasi tide gauge” points using the satellite altimetry observations, both of the problems mentioned above (i.e. weakness of the satellite altimetry observations and the chart datum determination based on the use of the coastal tide gauge observations only) can be solved. This will then lead to higher accuracy of the chart datum determination in the entire area. To achieve higher accuracy, observations of the tide gauge stations are also used. It is because the distances between the tide gauge stations and the time series obtained from the satellite observations are considerably large (sometimes more than 20 km). Therefore, this combination will lead to the establishment of a regular and continuous network of tide gauge observations in the entire area having acceptable accuracy. To determine this model, sea level variations due to the tide, polar motion variations, plates tectonic movements and all the factors affecting the potential tide with significant components (M2, S2, K1, O1) and even less important components such as signals with the period of 14 days, monthly, semi-annual, annual, 8.5 year and 18.6 year are considered in the case study area. It is also highlighted that because the satellite altimetry observations are only available on periods of 9.915 days, the high-frequency tidal signals cannot be detected using these data. Therefore, these frequencies are also included into the functional part of the model. Based on the above strategy, the separation between the elliptical reference and the chart datum has been computed by comparing the tide gauge data and the satellite altimetry data for the period of 2002-2005.

With the advent of the satellite altimetry in 1973, new scientific applications become possible in oceanography, marine sciences and Earth-related studies, and it was made possible to monitor the sea level with high accuracy and tidal modeling in a global scale. Advances in the sensors technology and different satellite altimetry missions in the recent years led to a great evolution in geodesy and the gravity field modeling studies. The oceanic tide is a periodic phenomenon of rise and fallen of the sea level that has emerged from the combination of waves with different periods. It is governed by the gravitational action of the solar system bodies essentially the moon and the sun, it translates by a transport of water masses. Tide phenomenon is of particular significance among researchers in different scientific societies such as Geodesy, Geophysics, and Oceanography. Since tide is one of the most effective factors on instantaneous sea level fluctuations, it is necessary to know the status of tidal in all offshore projects. So, we can have wider and more optimal exploitation of marine resources with known of the main components of the tide, and finally, the tide phenomenon and sea levels could be predicted with the exact knowledge of the tidal frequencies. Several methods, including Least squares method, Fourier analysis, and statistical methods, have been developed to determine the tidal frequencies. Fourier analysis is a convenient and efficient mathematical tool for modeling the behavior of a periodic phenomenon. In this study, main constituent frequencies of time-series and finally, tidal frequencies in Caspian Sea are determined using the Fourier analysis method relying on the concept of stationary time-series. In this way, first, for a closer look at the data and better visibility of other fluctuations in time-series, the trend component is removed from the data using the Fourier seriese. Next, the frequencies that make up the time-series were identified by the Fourier analysis and least squares method, and as mentioned, the concept of stationary time-series is used to find the main components of the tide in this study. Data from the altimetry satellite Jason-2 from 2008 to early 2014 is analyzed to form the instantaneous sea level time-series in 7 points in Caspian Sea that indicates large instantaneous sea level fluctuations. For time-series, the suitable model for tide modeling can be determined by the following equ ation: where is amplitude, is frequency, is related phase, and is the number of frequencies contained in the model. A discrete set of frequencies can be achieved by in a time-series with equidistant data, where is the time of the first observation and is related to the last. Nyquist Frequency is calculated as. So, the above equation can be written as: So, the amplitudes related to each frequency can be calculated using a least squares adjustment and much more effective frequencies contained in the signal can be identified based on these amplitudes. This analysis uses the concept of Aliased Frequency to calculate the main tidal components in 7 points in Caspian Sea, including: SSA، SA، S2، M2، MF، MM، S4 and M6 component. Finally, spectral analysis is used to study the effect of Volga River on the Caspian Sea level changes.

Nowadays, navigation is an unavoidable fact in military and civil aerial transportations. The Global Positioning System (GPS) is commonly used for computing the orientation or attitude of a moving platform. The relative positions of the GPS antennas are computed using the GPS code and/or phase measurements. To achieve a precise attitude determination, Carrier phase observations of GPS requiring the phase ambiguity resolution has been utilized. The more accurate the coordinates, the more accurate the attitude parameters will be. Attitude parameters are derived from the computed coordinates. Here, attitude parameters are computed by carrier beat phases of four single frequency GPS receivers. The problem of GPS attitude determination is an ill-posed problem if only GPS carrier phases are used. This is because the number of unknown parameters is always larger than the number of observations when the relative positions of the GPS antennas are computed. In this research, carrier beat phases of four single frequency GPS receivers are used to determine the orientation of a platform whose attitude parameters are already known. Observations are made for 10 minutes. In this research, two sets of constraints are used to fix the rank deficiency of the problem. The first consists of the Real Time Kinematic (RTK) coordinates of the GPS antennas. Fixed antennas to the moving body help add five additional constraints (second set) to the problem. These constraints increase the redundancy and make the least-squares estimation of the attitude parameters possible. Since the application of regularization methods contaminates the solution with regularization errors, application of the proposed constraints is superior to regularization techniques. This is practically shown through the comparison of the computed attitude parameters, a similar set of results which is derived using the Moore-Penrose algorithm as a regularization technique, and the reference values of these parameters which are provided through an independent research. According to the obtained results, 59 seconds is required to fix the ambiguity parameters. In other words, to reduces the accuracy of the float ambiguities to less than 1.0 cycle, their initial estimate should be updated by the next 58 measurement epochs. Then the ambiguity parameters are rounded to their nearest integer number. On average, the least squares estimate of the yaw parameter is 51.7000 with the standard deviation of ±0.01710. The average estimate of pitch is 39.1680 with the standard deviation of ±0.01540. Finally, on average, the least squares estimate of the roll is 26.1530 with the standard deviation of ±0.01370. Computed attitudes have been compared to their known values. By the new definition of the body frame given in this study, least-squares estimation of the attitude parameters would be possible even if only three GPS antennas are used. Computing the transformation parameters between the new and conventional body frames, attitude angles can be transformed to any conventional frame. The proposed method of this research is superior to the others. The computed biases represent the integrity of determination and corroborate usage of inner constraints and weighted parameters to resolve the rank deficiency of the problem.