نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال دوم شماره 4 (پیاپی 8، آذر 1390)

Using and integrating geospatial web services compatible with OGC standards are inevitable in GIS applications. OGC web services composition is possible by various approaches. One of the most common approaches to compose geospatial web services is by using orchestration languages. WS-BPEL language known as an accepted language for web service composition is an efficient orchestration language for common web services in industry. But due to the lack of support for OGC standards such as GetCapabilities, it is not an efficient approach to compose OGC web services. In this paper, we present an orchestration language compatible with OGC standards to tackle this problem. The language

Hyperspectral data, by precise sampling of objects reflectance in visible and near infrared spectrum in to numerous spectral bands, can prepare valuable data source for identification and recognition of different objects. One of the main applications of this data is classification for retrieving different land cover classes. However, numerous bands of this data, makes their classification a challenging task. Usual classifiers due to lack of sufficient amount of training datacannot have an acceptable performance.Thus, in recent years unsupervised classification algorithms have gained more attention. One of the emerging unsupervised algorithms in data mining context are kernel based fuzzy clustering algorithms (KFCMs),these algorithms, which are based on well known Fuzzy C-means algorithm and kernel function, usually, have better results for medical images and standard datasets. Dueto good performance of kernel based algorithms in hyperspectral image processing and the characteristics of FCM,the KFCMs algorithms seem good choices for hyperspectral data clustering. The objective of this paper is to study the performance of kernel based clustering algorithms for hyperspectral data clustering and comparing them with generic FCM algorithm. Because of great impact of kernel function on the performance of KFCMs

Object tracking in video sequences is a key topic in computer vision and can be viewed as lower level vision tasks to achieve higher level event understanding. Tracking of moving objects can be complex due to loss of information caused by projection of the 3D world on a 2D image, noise in images,complex object motion, non-rigid or articulated nature of objects, partial and full object occlusions, complex object shapes,scene illumination changes, and real time processing requirements. This paper presents a method that is motivated by the need to overcome some of the shortcomings of the existing detection and tracking algorithms. In this context, theAccumulative Frame Difference (AFD) has been used to detect the moving objects and a tracking algorithm based on a constant velocity motion model, and various cues for object correspondence has been applied to perform tracking moving objects. The potential of the proposed method was evaluated through comprehensive experimental tests conducted on a wide variety of datasets. Inspecting the results, the proposed method has the potential to track moving objects effectively and efficiently.interpreter, or in other words orchestration engine is implemented to interpret and execute proposed orchestration language. In fact, the language interpreter acts as an OGC web processing service to execute and orchestrate OGC services. Finally the proposed method is demonstrated through a web service chaining for site selection analysis.

Most energy existed in the earth are dependent to the Sun directly or indirectly.Energy reserves can be divided into nonrenewable and renewable energies. Renewable energy is energy which comes from natural resources. In this research we studied of extracting energy from tides in the northern Persian Gulf coast was investigated in five stations (Shahid Rajaee,Bandar Imam Khomeini, Imam Hassan,Kangan and Bushehr). In this research, the field data collected in January 2009 is related to the tides. Then, with the help of TTide software which can be run in MATLAB software, amplitude and phases relating to the 30 principal components were extracted and signals were filtered by Bandpass technique. By considering the 4 main components of K1, O1, M2, S2 the amount of energy produced at each station as well as the kind of tide were recognized.In the case of stations the kind of tides are semidiurnal and the maximum amount of potential energy is 1.81 × 105 W that related to Shahid Rajaee station. The predicted components were compared with Forman's software results which used for harmonic analysis in the National Cartographic Center (NCC), that show good consistency.

Precise and update information of urban features are being increasingly demanded for various applications of modeling and planning of urban environment.Hyperspectral imagery is one of the most efficient tools to obtain the physical and spatial information in such a complex area. Indeed, the hyperspectral systems are able to collect the valuable image data, which can be used for detection, classification,identification and quantification of manmade or natural phenomena. Target detection technique is one of the reliable and efficient approach aims to extract information about class (es) of interest.In this paper, we propose an analytical framework to produce the accurate landcover map of building roof print from high spatial, hyperspectral imagery. The proposed approach consists of several steps. The first step attempts to reduce the undesirable noise and spectral correlation of hyperspectral images by using the MNF (Minimum Noise Fraction) algorithm. In second step, different spectral matching techniques are employed to obtain the target maps. Spatial information is also used to improve the detection accuracy. Finally, ANFIS (Adaptive Neuro-Fuzzy Inference System) method is used to fuse the detection results and arrive to land-cover map of building roofs’ materials. The results of quantitative and qualitative assessments of experiments show that the proposed algorithm can lead to acceptable accuracy for mapping of urban features.

Considering the development of Remote Sensing sensors, Polarimetric images have been the matter of interest as powerful and efficient tools; since they provide highly informative sources of terrain targets utilizing different polarizations of electromagnetic waves. Classification of a Polarimetric image is an important step to extract such information. Among the several classifiers,Support Vector Machines (SVMs), for their operation based on geometrical characteristics, are particularly more attractive cases in Polarimetric image classification. Although SVMs classifier has superior performance in high dimensional search spaces, redundant or irrelevant features have significantly affect the classification accuracy and the speed of convergence. Therefore, determination of optimum features to achieve an accurate SVMs classifier is an important challenge.Since traditional optimization techniques usually have computational complexities and trap in local optimum in a large search space,Meta-heuristic Algorithms which perform exploration and exploitation to obtain global optimum are applied in this research. Thus,the potentiality of Simulated Annealing Algorithm as a powerful optimization technique in determining the optimal subset of polarimetic features is evaluated in this paper. Also, Genetic Algorithm as a classicmethod is used to be compared with Simulated Annealing Algorithm. The results demonstrate the superior performance of Simulated Annealing Algorithm in terms of classification accuracy and speed of convergence in comparison with Genetic Algorithm.