نشریه علوم و فنون نقشه برداری
سال چهارم شماره 3 (پیاپی 15، بهمن 1393)

Automatic extraction of road from satellite images is one of the most important researches in the field of remote sensing. The method proposed in this study is based on a fuzzy method for detection of road areas from high resolution SAR images. In this method, the multiple features are extracted first based on the backscatter coefficient of each pixel and its neighbor pixels from the input image. The extracted features are combined with each other in the next step using a fuzzy algorithm and the desired road areas are selected separately in the last step considering the spatial and spectral criteria. The favorite results and root mean square of 78% were obtained by applying this algorithm on high resolution SAR images obtained from the TerraSAR satellite.

Nowadays building three dimensional models using a sequence of images of the scene is an important issue in close range photogrammetry known as videogrammetry. In this paper, we propose an efficient absolute orientation of video frames method for urban 3D reconstruction and mapping. Generally, we have five steps for 3D reconstruction from video sequence: camera calibration, key frames extraction, relative orientation of key frames, absolute orientation of key frames and dense 3D reconstruction. We suppose the calibration step and key frames extraction step are done beforehand and our purpose is absolute orientation of video frames with an effective method. The method uses videogrammetric solution in combination with surveying for absolute orientation. After relative orientation of video frames and creating an adjusted model from the scene, absolute orientation is done using the control points just in first and last frames. The results in real scene shows the efficiency of the method.

Abstract In a navigation system, map visualization based on vehicle’s location and environment plays an important role. Therefore, it is necessary to visualize useful information in an optimum method. One of the most important issues in mobile GIS applications and almost all location-based services is to serve an efficient and useful map for user. To this goal, the main idea from context awareness systems can be provided. In the framework of this paper, visualization method for navigation tasks is developed. The essential idea of this research is to use context-aware properties in map visualization. The main feature in such systems is to represent maps with respect to user requirements and specially user location. The idea is serving maps to user by focusing on user’s position so that area which is closer to user is displayed in larger scale with more details and the area some far around the user with smaller scale with less information content will be displayed. In most cases user needs to have a global view of whole environment and also needs to focus on specific position. So using a zoom window with fixed scale to display the user's position could not be a complete answer to this need. In this paper, we utilize the fisheye projection as a solution improving location-aware map visualization. Results show that fisheye projection allows focus on user location, while maintaining the relationship associated with that part of the map with the surrounding area. Also in contrast to zoom window with fixed scale, fisheye projection with spherical aberration, preserve the integrity of the information. Our practical work; shows usefulness using such projection system for displaying map in a navigation system.

LiDAR is a recent and progressive technology for collecting data from surface that operates based on the laser length measurements. High planimetry and altimetry accuracy of the obtained LiDAR point-cloud, as well as the ability to record intensity are the reasons to utilize LiDAR data for detecting objects. Extracting roads as both important urban objects and connection channels of a country is vital significantly. In this paper, a hierarchical approach was proposed for extracting the main road network with acceptable precision. The proposed method eliminated non-road objects by using range and intensity data and applying some filters successively. Also, it prevented to produce gap and fracture in the road network. In this regard, firstly, three features were produced by specifying a threshold on the last intensity pulse and utilizing the last range pulses to obtain nDSM, as well as producing slope with normal vectors. The linear convolution of the produced feature layers was computed to obtain an initial road class. Subsequently, it was tried to remove noises from the initial road network and improve detection results according to the road geometrical characteristics. Finally, the skeleton morphological filter and Fourier features were used to smooth roads boundaries and to eliminate byroads. The evaluation results of the road extraction using our proposed approach achieved 80.56% Correctness and 77.82% Completeness. Generally, we tried to use all parameters that are useful for separating roads from other objects in order to extract the main road network with high accuracy and speed by applying them successively.

In this paper, 3-layer perceptron Neural Network has been used with 5 neuron in hidden layer for modeling the Ionospheric Total Electron Content (TEC) Over Iran. For this purpose, 25 GPS station from IPGN is used. These 25 stations are located within a range of approximately 24oN to 40oN and 44oE to 64oE. Evaluation of the results has been applied with 1 GPS station in Tehran. The station is equipped with ionosonde. So it is possible to calculate independently the TEC at the station. Minimum relative error obtained from evaluation is 0.73% and maximum relative error is 34.66 %. In this research, for the evaluation of artificial neural networks in estimating the TEC, a polynomial of degree 3 with 11 coefficients are used. Comparison of the relative error from polynomial model and relative error from neural network, illustrate the superiority of the neural model with respect to polynomial in this region. The number of neurons in hidden layer of neural network and the order and coefficients of the polynomial used in this paper is determined by trial and error, and by taking the minimum relative error for the results.

To know how coordinates of points on Earth surface are changing by time is very important for geodetic applications. At first step, the paper presents a time dependent model for point displacements caused by plate tectonics in ALBORZ Mountains, then displacements for points of ALBORZ classical geodetic network will be estimated. This estimation is performed for points without continuous GPS observations. Velocity vectors of an independent research based on GPS observations between 2000-2008 are used as constraints of this model. Modeling is based on analytical relations of OKADA. In this research, using a method of inverse genetic algorithm, dislocations and depth fault are computed. Minimum root mean square errors of the model for interseismic displacements is about 0.97 mm/yr. The total displacement of each station is obtained by summation of the vector of the modeled interseismic movement and displacement vector fields for earthquakes with magnitude which are computed directly by OKADA relations. Maximum amount of changes of the coordinate stations of the 25 year old classical geodetic network in ITRF frame approximately is 80 cms which cannot be neglected.

Phenomena in the real world are grouped into two categories: static and dynamic. The static phenomena don’t change with variation of the time but the dynamic phenomenon will do it so. These changes may be occurred in the phenomena property like spatial or geometry. Traffic is a Spatio-Temporal phenomenon because its properties which are referring to some locations on the earth will change in respect to the time variation. Today, due to the development of technology, the management capability of the spatial-temporal phenomenon has been provided in a Temporal Geospatial Information System (TGIS). The objective of this research is study of using TGIS for controlling and coordinating of traffic signals. For this purpose, first, parameters and components for determining traffic condition and coordinating traffic signals are collected. Then based on these data, the structure of the traffic data is determined for entering into a database. This structure determined in such a way that the traffic condition is introduced to the system based on the type of existing traffic behavior. In this study, the dynamic data along with spatial data of street path (static data) are stored and structured based on relational model and Time Stamping Spatial Objects model. With performing relation between these data the instant variations of the traffic condition is determined. Based on these variations the traffic parameters and offset between different intersections are computed and then based on this information the schedule of traffic signals will be implemented with a singular or network method.

In the recent decades, the determination and evaluation of geometrical correction models as well as georeferencing satellite images have been of great consideration due to their frequent use in various fields, and are regarded a leading topic in photogrammetry and remote sensing. This paper is about the geometric correction of the Worldview-2 satellite image using different modeling methods and tries to give an overall evaluation of strength of various possible modeling for a prototype image of an urban area like Tehran. The distribution and number of control points with regard to their effects in each modeling method were examined which resulted in a high precision of a final geometry correction about 0.36 meter using rational functions. For more optimization artificial intelligent methods like genetic algorithms and neural networks were used. With the use of perceptron network, a result of 0.71 pixels with 4 neurons in middle layer was gained and the final conclusion was that with these algorithms it is possible to optimize the existing models and have better results than usual ones.

Human activities are embedded in the space. People frequently use their spatial cognitive abilities in familiar spaces or navigational aids (e.g., map and Satnav) in unfamiliar areas to position and find their ways. Nowadays, satellite navigation systems are widely used by even non-experts; people find it efficient and easy to use. These systems do positioning, way finding and guidance in a nonstop and reliable way. In other word they make it easy to navigate, it is where the problems come from; people have to get depend on these systems and lose their innate ability to navigate without any non-environmental aids. This paper is intended to merge the bests of various navigation methods to have a cognitively enhanced navigation aids which could promote people spatial cognition.

The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus and, then, avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this article, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The dimensions have been defined as fuzzy sets in order to handle the data uncertainty. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence, while SO2 and O3 have no effect on that.

Nowadays, dust storm in one of the most important natural hazards which is considered as a national concern in scientific communities. This paper considers the capabilities of some classical and intelligent methods for dust detection from satellite imagery around the Middle East region. In the study of dust detection, MODIS images have been a good candidate due to their suitable spectral and temporal resolution. In this study, physical-based and intelligent methods including decision tree, ANN (Artificial Neural Network) and SVM (Support Vector Machine) have been applied to detect dust storms. Among the mentioned approaches, in this paper, SVM method has been implemented for the first time in domain of dust detection studies. Finally, AOD (Aerosol Optical Depth) images, which are one the referenced standard products of OMI (Ozone Monitoring Instrument) sensor, have been used to asses the accuracy of all the implemented methods. Since the SVM method can distinguish dust storm over lands and oceans simultaneously, therefore the accuracy of SVM method is achieved better than the other applied approaches. As a conclusion, this paper shows that SVM can be a powerful tool for production of dust images with remarkable accuracy in comparison with AOT (Aerosol Optical Thickness) product of NASA.

Tehran, capital of Iran, is located on few known (Mosha, North Tehran Fault and South and North Ray) and unknown faults which expose this mega city to huge earthquakes’ effects. In addition to considerable seismic hazard in Tehran the existence of old and non-standard buildings make the repercussions even worse. Determining locations and intensity of seismic vulnerability of a city is considered as a complicated disaster management problem. As, this problem generally depends on various criteria and expert’s opinions, one of the most important challenges concerned is the existence of uncertainty regarding inconsistency in expert’s view. Uncertainty in seismic vulnerability map would results biases in risk management which has multilateral effects on decision makings. Some multi-criteria evaluation methods have recently been proposed to handle some aspects of uncertainties in the process of producing the seismic vulnerability map for Tehran. Granular computing approach is proposed in this paper to overcome the limitation of the abovementioned existing algorithms. It can be regarded for learning classification rules by considering the two basic issues: concept formation (making granules) and concept relationships identification (relationship between granules). One of the most important features of this method with respect to previous studies is inference of more compatible rules having zero inconsistency extracted from existing training databases. Furthermore, in this approach, non-redundant covering rules will be extracted for consistent classification where one object maybe classified with two or more non-redundant rules. In this study the result of north Tehran fault hazard analysis is applied to the vulnerability assessment process and activation of other faults have been ignored. It is assumed that the northern fault of Tehran is activated and then the classification rules of seismic physical vulnerability are inducted from granular computing tree. A pilot area of Tehran Metropolitan Area located in the north of Iran was selected for the purpose of this study.

Existing location allocations approaches do not take into consider the changeability nature of the world. This happens, whereas the most location allocation problems face time changing parameters. The change of these parameters can be foreseen sometimes so they should be token into consider in location allocation by modeled approaches. This paper has formulated the location allocation problem of p-median type dynamically and proposed an approach for solving dynamic location allocation and resource allocation problems based on artificial intelligence. A sample problem of NP-Complete type has been propounded and solved in dynamic and static modes in order to evaluate performance of the proposed approach. The implementation results has verified performance of the proposed method and dynamic location allocation approaches in respect to static ones for solving location allocation problems using change of demand model in time.

This paper introduces an approach to detect and 3D reconstructs buildings using aerial imagery and LiDAR data. This research consisted of three phases, building detection, 2D building outline reconstruction and 3D building reconstruction. In phase building detection, firstly off-terrain objects including trees and buildings are extracted from LiDAR data. Secondly Support Vector Machines (SVMs) Algorithm is employed to differentiate trees and buildings. Training data which are used in SVMs are choose in semi-automatic procedure. After eliminating trees, K-means clustering algorithm is used to separate buildings which are not in same elevation. Results are showing our building detection method was successful in detection of small and large buildings. Completeness, Correctness and Quality for building detection results respectively are 86.60%, 99.10% and 85.92%. In phase 2D building outline reconstruction, firstly, the building boundaries have been vectorized. Then produced boundaries are generalized and unnecessary line segments are removed. After generalization, a new approach is used to build orthogonal buildings. In this research, 3D building reconstruction is done in LOD2. For detection of roof structure of buildings, the parameters of plane that have been fitted to LiDAR points within each kernel is obtained. With considering these parameters as features of each building, ISO-Data clustering has been done. The results of this clustering represent the planar surfaces of each building. So, a plane is fitted to each class (planar surface) with least squares method. Then, within the boundaries of each building, roof patches which have similar plane parameters and are close together are merged. Plane parameters of integrated roof patches again are determined. Finally, 3D models of buildings have been reconstructed with intersection of planar surfaces and obtaining of vertex points of each building. Elevation and total RMS values of specified planes for roof structure of buildings respectively are 0.4 m and 0.9 m.

Solar radiation is one of the most salient factors in determining the optimal locations of solar farms. It is the main input of geological, ecological, meteorological and hydrological models. In Iran, there are 63 stations which measures solar radiation; compared to the extent of the country, solar radiation monitoring network has very low densities. In the present study, in order to increase the network congestion and continuous mapping of solar radiation, synoptic meteorological stations’ data were used. Considering the high correlation between solar radiation and meteorological data (sunshine duration, maximum temperature and negatively high correlated sea pressure), such data was used to calculate solar radiation in synoptic stations by using Fuzzy Inference System (FIS), Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS). The evaluation of the results was performed by RMSE, MAE and MBE to rank the methods. Our results revealed that Sugeno method accompanied by Fuzzy C-mean clustering has RMSE=28.07 w/m2 that lays the least errors amongst the others. With respect to ANN, Cub-clustering and Grid partition ANFIS, Sugeno method showed 18, 39% and 42% improvement. MAE and MBE also implied the ability of the Sugeno fuzzy method. Such a method is more flexible for modeling complex and nonlinear systems. The implementation of the methods in prediction of solar radiation revealed that Sugeno is easier and faster to executable. Estimated Solar radiation for 333 synoptic stations was interpolated by Ordinary Kriging to generate a continuous surface for the country. The generated solar radiation atlas is suitable to identify solar throw areas of our country as well as for engineering applications and energy planning. Radiation atlas showed that 32 percent of the country has solar radiation above 500w/m2 that is the amount of radiation required for solar farms.

A spatial trajectory is a record of moving object’s spatial changes through time and is modeled by a sequence of discrete points with spatio-temporal coordinates. Increasing number of moving objects and positioning technologies resulted in immense number of spatio-temporal data needing various analyses. Extracting similar trajectories is one of the crucial analyses in spatial trajectories. So far various distance functions have been proposed for measuring similarity where each one has addressed similarity from its own point of view and is suitable for particular data with special characteristics. Thus, functions effectiveness is not the same for all kind of data and applications and understanding capabilities and characteristics of functions is the prerequisite of choosing the suitable function. In this paper, a comparative experimental study is conducted on the effectiveness of seven widely used trajectory similarity measures which are the base of many other former proposed distance functions and their advantages and drawbacks are discussed.

One important source of errors on GNSS signals is the ionospheric effect. This layer of the atmosphere is filled with charged particles. Ionospheric effects on the waves are dependent on the amount of TEC. This paper uses the least square harmonic estimation (LS-HE) that is one of the analytical methods in the frequency domain. We used the vertical TEC values obtained from GIM models with global cover provided by the JPL analysis center. We use 15 years of bihourly data gathered from the 152th day in 1998 to the first day of 2014. We first determine the important periodic signals by applying the univariate and multivariate harmonic estimate on the TEC time series. The multivariate analysis revealed the presence of daily periodic signal with its higher harmonics and annual period with its higher harmonics. We then calculate the spectral power of a number of identified signals in all available data range. The result indicate that the higher harmonics of the daily signal (tri and quad diurnal) show their maximum spectral values in the dip equator. This indicates that the earth's magnetic field is one of the cause, to these provide patterns.

Due to wide spread usage of the satellite positioning techniques especially GPS, we need to precisely determine geoid model in order to use GPS measurements for height determination, as an alternative of traditional leveling techniques in geodetic applications. Precise local geoid modelling using GPS/Leveling data, apart from the existing models such as geopotential models and gravimetric geoid models could be an interesting investigation topic. An important question is, ‘What accuracy level can be achieved using this approach?’ However precession of this modelling could be influenced by some issues such as data quality or modelling techniques. In this paper, we attempt to assess the implementation of modern learning-based computing techniques including artificial neural networks and adaptive network-based fuzzy inference systems compared with multivariate polynomial regression equations in GPS/Leveling Geoid modeling. This assessment carried out in a small and dense network of GPS/Leveling benchmarks in contrast with previous studies, located in shahin-shahr, Isfahan. And these high quality data make it possible to achieve an accuracy of better than 1 cm. The results show a few millimeter superiority of ANN and ANFIS derived geoid models in terms of root mean square error, as well as in terms of coefficient of determination. And RMSE=8cm, R2=0.9949 and RMSE=7cm, R2=0.9964 achieved for this models respectively. Therefore ANFIS derived geoid model provide the most accurate geoid heights in the study area.

Today, various Unmanned Aerial Vehicles (UAV) Photogrammetry systems have been developed and utilized for close range aerial image acquisition and 3D topographic mapping. They have several advantages compared with classical manned aerial photogrammetry systems such as less cost, more accessibility, higher safety, shorter data acquisition process, and requires less skilled persons and specialized equipment. In this research a low-cost ultra-light UAV Photogrammetry system is used which equipped with a non-metric low-cost digital camera and a semi-automatic navigation system. Due to using these low cost components and general ultra-light UAV instability, some geometric and radiometric problems in thousands of small sized UAV aerial images are happen. The most important problems are (1) high tilt and rotation of vertical aerial images (2) Irregularity in image arrangement makes non-conventional standard overlap/side-lap coverage, (3) lower-quality images due to illumination changes, image motion and low signal-to-noise ratio, (4) high geometric and unstable image distortions come from non-metric off-the-shelf camera. In addition, since conventional photogrammetric software are designed and worked based on the characteristics of images taken from metric cameras mounted on manned aircraft, it is impossible to do automatic feature extraction and matching successfully on UAV images. These problems have caused to a big gap between imaging and aerial triangulation steps. The main goal of this research is dissolving the mentioned gap by automatic image processing of images were captured by ultra-light UAV and prepare input data to photogrammetric software for aerial triangulation. The results of this research show by eliminate this gap, relative high precision can be achieved from photogrammetric block bundle adjustment of these images. Also the results show that providing spatial products of these images such as Digital Terrain Model and Ortho-photo mosaic is possible.

With recent technological advances in remote sensing sensors and systems, very high-dimensional hyper spectral data are available for a better discrimination among different complex land-cover classes. However, the large number of spectral bands, but limited availability of training samples makes the problem of Hughes phenomenon or ‘curse of dimensionality’ in these data. Moreover, these high numbers of bands are usually highly correlated and the information provided can contain several data redundancies. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, classifier ensemble systems may have better performance than single classifiers especially on hyperspectral data with this high level of complexities. This paper presents a new method for classification of hyperspectral data based on a band grouping strategy through a SVM ensemble system. Proposed method used a band grouping process based on a mutual information (MI) strategy to split data into few band groups. After band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, proposed method applied SVM on each band groups that produced in previous step. Finally, this paper applied Naive Bayes (NB) as a novel and robust classifier fusion method for combining classifiers in classifier ensemble system. NB is a precise classifier fusion based on the concepts of Bayesian theory. Experiments are applied on two common hyperspectral data. Obtained results show that the classification accuracy is significantly improved by the proposed method in comparison with standard SVM on all bands of hyperspectral data. Also, these results confirm the high performance of band grouping strategy in contrast to using of standard SVM on all feature space.