JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2015 | Volume:5 | Issue:2|Papers Count:21

Forest species classification has become increasingly important due to changing environmental conditions and the need to sustainably manage forest resources. They have an important role in the global hydrological and biochemical cycles. Remote sensing has been shown to be valuable for forestry applications such as monitoring, forest inventory, biophysical variable estimation, species discrimination or classification. In this regard, polarimetric synthetic aperture radar (PolSAR) data can provide information on the structure of forests. Moreover, recent studies show that image classification techniques, which use both scattering and spatial information, are more suitable, effective, and robust than those that use only spectral information. The main motivation of this paper is presenting an appropriate contextual classifier for PolSAR data.Relating the posterior Markov random field (MRF) energy function to the support vector machine (SVM) classifier, the proposed method takes advantage of both the parametric and nonparametric classifiers which efficiently combines SVM and Wishart classifiers. The proposed contextual image classifier adopts the ICM approach to converge to a local minimum and represent a good tradeoff between classification accuracy and computation burden. In proposed method, the computational cost of the training stage is exactly similar to that of training stage in SVM. However, for classifying test pixels, computational cost of the proposed method is the sum of those of SVM, Wishart, and also MRF.The method allows taking into account various types of features. In particular, the features obtained directly from original data, the features which are derived using the well-known decomposition methods, and the SAR discriminators are used as input features. Moreover, the covariance matrix of the PolSAR data and the Wishart distribution are used to compute the MRF energy function. The proposed method modifies the decision function and the constraints of SVM based on the integration of contextual information. Selection of the appropriate features and optimization of requiring parameters perform simultaneously using genetic algorithm (GA).In this study, two Radarsat-2 polarimetric images acquired in the leaf-off and leaf-on seasons are used from a forest area. A total of six classes, including white pine, red pine, poplar, and red oak are cinsidered. Two other classes in this study are water area and ground vegetation region.In this study, the selection of the appropriate features and optimization of requiring parameters are performed simultaneously using GA. Then, the proposed algorithm is compared with the Wishart, Wishart-MRF (WMRF), and SVM as the baseline classifiers. Comparison of the accuracy of the proposed method with baseline methods is performed. The results show that this algorithm allowed approximately 16%, 11%, and 7% increases in overall accuracy with respect to the Wishart, WMRF, and SVM classifiers, respectively. Moreover, proposed method allows 25.29%, 19.74%, and 11.76% increase in average accuracies of forest species with respect to the Wishart, WMRF, and SVM classifiers, respectively. This demonstrates the efficiency of the proposed method for classification of forest species. Also, the results show that, incorporating contextual information into proposed method significantly improves the spatial regularity of the classification results and reduce the sensitivity to noise or speckle.

In this paper, we propose an unsupervised method for change detection in polarimetric synthetic aperture radar (PolSAR) images. The symmetric revised Wishart (SRW) is applied for measuring the similarity of two multilook complex (MLC) covariance matrices. The SRW produces a scalar feature image as an input into the automatic thresholding algorithm which is aimed to distinguish change from no change. In particular, the Kittler and Illingworth minimum-error thresholding method is generalized to model the non-Gaussian distribution of change and no change classes. Experimental results on bi-temporal simulated and RADARSAT-2 C-band PolSAR data confirm the effectiveness of the proposed method. The results of the real data also demonstrate that the multipolarization SAR improves the detection accuracy and lowers the overall error rate of the method compared to single-polarisation SAR.Introduction: Multitemporal satellite remotely sensed data from a geographical area offers a great potential for monitoring and detecting changes in Earth’s surface (for e.g. damage assessment in natural disasters [1], monitoring the changes in agricultural areas [2], and glacier change detection [3]). Synthetic aperture radar (SAR) is an important instrument in remote sensing, providing measurements insensitive to the sun-light and atmospheric conditions. Furthermore, polarimetric synthetic aperture radar (PolSAR) sensors provide data with increased discrimination capability as compared to single-channel SAR and also insensitive to the sun-light and atmospheric conditions.Several change detection algorithms in SAR data has been developed in the literature, e.g., [4]-[5]. Unsupervised change detection is generally performed in three steps: 1. image preprocessing including co-registration, speckle filtering, and radiometric and geometric terrain corrections, 2. comparing SAR image pairs with a desired test statistic, and 3. finally, a thresholding method is applied to the test statistic to achieve the final change map.Fig. 1: the block diagram of the proposed change detection approach In the analysis of change detection in multilook complex (MLC) PolSAR images, the backscattered signal is represented by the so-called polarimetric sample covariance (or coherency) matrix. In [2], the Wishart likelihood ratio test is proposed as a test statistic for change detection in multilook PolSAR images. Akbari et al. utilized the complex kind Hotelling-Lawley trace statistic as a new test statistic for change detection in multilook PolSAR images [18]-[19]. In [20], Ghanbari et al. applied the symmetric revised Wishart (SRW) distance in [8] as a test statistic for detecting the changes between two Wishart distributed multilook covariance matrices.Changes are finally detected by a decision threshold to the test statistic to distinguish change from no-change. In the present paper, the thresholding is performed using the generalized Kittler and Illingworth’s minimum-error algorithm (K&I for short) [10] on the SRW image. In the proposed method, the generalized Gamma distribution, denoted GΓD, is applied for modeling change and no-change classes in the SRW image. The GΓD was first proposed by Stacy [15] and has been widely applied in many fields, e.g., [14]. This distribution has a highly fixable form and good fitting capability to the histograms of change and no-change classes. Parameters of the probability density function (PDF) in this study are estimated using the method of log-cumulants (MoLC). This estimation method has been adopted in the analysis and processing of SAR images, e.g., [16]-[17]. The block diagram of the proposed unsupervised change detection method is presented in Fig. 1.

During the time, landcover and associated landuse patterns are changing very fast and the human factors play a major role in such drastic changes. Scientists have formerly attempted to identify the landuse altering processes and related environmental impacts. In the recent studies, evaluating the agricultural outputs and arable lands is regarded so important that their organization and management can be mentioned as a very critical factor for all countries. Nowadays, satellite images could be accurately processed, as an advanced technique in remote sensing, to determine the environment changes in a particular object of study between two or more time periods. Therefore, favorable results are achieved in recognition pattern-based remote sensing methods in order to achieve these global aims. Although there are various methods in photography and remote sensing for dealing with revealing changes, none of them can be considered as an optimum one completely. In the present paper an appropriate Supervised approach was proposed in odrer to identify changes in semi-urban areas based on both neural network algorithms and SVM (Suport Vector Matchene). To achieve this purpose, Landsat7 multi-temporal images are applied. In principle, this method unlike conventional ones, is not introduced to identify changes, but our method can be addressed as determining changes without comparing multi-temporal single-source images with each other and principally relying on color fusion (fusing colors in different bands and creating a different color) in the resulted single image which contains all the layers of two multi-temporal images. The main basic idea is to produce a multi-temporal single-source image using two images and then using color fusion and pattern recognition methods on the georeferenced single-sourced image, afterward, was produced a map for changed and unchanged regions, finally, was applied algorithm on image to provide the final change map. Simplicity and increases performance can be proposed as the advantages of this method. In fact, mixed collective color (color fusion) method with pattern recognition methods or classification methods and using them for rsulted reference single image the basis of this method in order to identify the changed and unchanged zones. Finally, our main idea was based on that after selecting training data from one single (common data in both images), use training data in unmodified and stable zones and remove the data which located in changed zone. In practice, after revealing the modified zones showing an overlap with training set data, the existing data in mentioned zones were removed. Finally, applying training data and conventional classification methods such as SVM and neural networks classes were identified and introduced and final map of changes developed. Our achieved results suggest that this approach is far better than traditional methods and significantly reduces training samples and increases accuracy (2.5-3 percent), pace and spectral information for performed classification.

Extraction of spatial relationships between georeferenced layers is one of the important objectives in spatial modeling in Geographic Information Systems (GIS). In last decades, a lot of techniques have been proposed for spatial modeling. Among them, Computational Intelligence techniques have been successfully employed in a wide range of spatial modeling.Most Computational Intelligence techniques automatically solve problems with requiring the user to know or specify the form or structure of the solution in advance. However, in most cases determining the structure of the solution in advance is difficult and may lead to inaccurate results.In order to overcome this challenge Genetic Programming (GP) which is a systematic, domain-independent method inspired by evolution is applied. In GP, user is not required to specify the structural complexity of the solution in advance but the algorithm tries to find an explicit relationship between the input and output. GP uses a tree-structure which captures the executional ordering of the functional components within a program: such that a program output appears at the root node; functions are internal tree nodes; a function's arguments are given by its child nodes; and terminal arguments are found at leaf nodes. However, GP can have a tendency to find solutions that are biased towards the training set (Overfitting). In this research we proposed a new method for limiting the effect of overfitting in GP. Also, for achieving more accurate results we use multigene GP in which individual consists of one or more trees.At the final stage, Sensitivity Analysis (SA) which is the study of how uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input is used to determine what inputs, parameters or decision variables contribute more to the variance in the output of a model. There are three types of SA: 1-Screening SA, 2-Local SA and 3-Global SA. Screening SA methods are approximate but with low computational cost. When dealing with models containing large amounts of uncertain input factors, screening methods could be useful because they are able to isolate the set of factors with strongest effect on the output variability by very few model evaluations. A drawback of this feature is that the sensitivity measure is only qualitative which means the input factors are ranked in order of importance. Local SA looks at the local impact of each factor on the model output. The input variables are basically changed one at a time and the impact of this individual parameter perturbation on the model output is calculated using local sensitivity indices. A drawback of this feature is that the method does not work when the model is either nonlinear or several input factors are affected by different uncertainties. In global SA, both relative contributions of each individual parameter and the interactions between parameters to the model output variance are simultaneously evaluated by varying all input parameters simultaneously over the entire input parameters space. Several types of global SA, such as partial rank correlation coefficient, multiparametric sensitivity analysis, Fourier amplitude sensitivity analysis (FAST) and Sobol’s method have been used successfully in different models. Among different methods of sensitivity analysis, Sobol’s and EFAST as the methods of variance-based are employed.The proposed method has several applications in spatial modeling issues. As a case study, the proposed procedures were applied to produce mineral potential map of Aliabad copper deposit. Results indicate that total field intensity criterion has the most effect and lithology has the minimal impact on the mineral potential mapping.

In recent years, the growth of population, increase in environmental pollution, and changes in human’s life style, a dramatic increase in the number of asthmatic patients has been observed. In addition, the Lack of suitable solution for the cure of asthma shows us the necessity of recent researches for controlling of the exacerbation of asthma in order to improve patients’ quality of life. Environmental condition in a spatio-temporal perspective enables us to do some predictive analytics in order to providing this ability to the asthmatic patients to have self-checking in addition to the medication by personal physician. Ubiquitous patient monitoring system which is enabled by geospatial perspective can assist us to provide such services to the asthmatic patients in both outdoor and indoor environments. In this paper, we monitor ubiquitously asthmatic patients in a Ubiquitous GIS environment by considering 8 important environmental asthma triggers including CO, NO, O3, PM10, SO2, Temperature, Humidity, and Pressure, and 4 medical records of current status of asthmatic patients. In fact, to achieve a better modeling with divided our research to two parts: User Model and Contextual Model. For implementation of Ubiquitous Asthma model (UbiAsthma), this survey is done by consideration of 30 patients in Tehran city. To develop our prediction model in UbiAsthma, at first we used VIKOR method to reclassified current patients’ medical classification to a novel and applicable classification for providing context-aware services. Secondly, after classification of patients, one patient is selected and the information related to FVC and FEV1 of the patient in 86 different locations within 47 days is collected. The patient was equipped by CO pollutant sensor (MQ9) and Temperature, Humidity, and Pressure sensors collection by smart-phone in order to having of a real-time database for the patient’s trajectory. Also, we utilized O3, NO, SO2, and PM10 for all 86 locations as static database in our model. After data collection and manipulation, Artificial Neural Network (ANN) is used for doing predictive analysis for asthmatic patients’ status. The result of test and train of ANN method shows that UbiAsthma model has 0.0230 evaluation error in prediction of asthmatic patients’ status, which is a considerable output in our model.

The purpose of Multi-modal Multi-criteria Personalized Route Planning (MMPRP) is to provide an optimal route between an origin-destination pair by considering weights of effective criteria, in which this route could be a combination of public and private transportation modes. In recent studies, the weighted linear aggregation rules were developed to calculate the impedance of links that were high tradeoff decision strategies. A decision strategy defines whether a user insists on satisfying all of his/her preferences regarding the selection of one route from a set of routes or he/she would be happy if the most of criteria would be satisfied. In this paper, a fuzzy analytical hierarchy process (fuzzy AHP) and quantifier-guided ordered weighted averaging (Q-OWA) operators were integrated to calculate the impedance of links. Fuzzy analytical hierarchy process (fuzzy AHP) weighting method by Saaty embeds fuzzy theory to basic AHP method. In AHP weighting method, a matrix-liked structure is considered by pairwise comparison between criteria with exact numbers. Despite the general popularity, the AHP method is not capable to consider the users’ ambiguity and the lack of clarity in their preferences. To solve this problem of the AHP method, the fuzzy AHP method was proposed to use fuzzy numbers rather than exact ones in pairwise comparisons. In this research, the triangular fuzzy numbers was used for these pairwise comparisons.To model a family of parameterized decision strategies, Yager introduced the ordered weighted averaging (OWA) operators. This method calculates the user’s risk taking and risk aversion, as well as enters them for selecting the final option. Quantifier-guided OWA is obtained by integrating the fuzzy linguistic quantifiers with the OWA operators. In this study, a class of relative quantifiers, called “Regular Increasing Monotone (RIM)” was used. The main characteristic of these methods is supporting the different decision strategies in calculating the impedances. In this method, the user determines the relative weights with fuzzy AHP method at first. Then by considering his/her desired decision strategy, impedance of the links were calculated. The proposed model can also propose the robust personalized route under the different decision strategies. By considering the different decision strategies, this model provides different routes. Then, by determining minimum and maximum impedance of each link, the model presents the robust personalized route. In this study, subway, BRT, bus, taxi, and walking transportation modes were considered for traveling between nodes. Moreover, time, length, and user’s bother of each transportation mode were considered as effective criteria. This model was implemented in an area in the center of Tehran. The considered area had 21 km2 and consisted of 2 BRT lines, 28 sweep bus lines, and 4 sweep subway lines, and totally more than 45 km of roads. The proposed method was implemented for one of the most crowed path in our case study, i.e., a path from Baharestan square to Enghelab square. Initially, the devoted configuration wizard for the pairwise compression were presented to 45 users (9 users for any decision strategy) and they were asked to weight the criteria. Then, the relative importance of each criterion was calculated by considering the weights assigned within the pairwise compression matrix. Afterwards, they were asked to determine their desirable decision strategies, including “at least one”, “half”, “many” and “all”. Results showed that on average 80.66% of the users with different decision strategies, selected the model proposed route as the best route.

Nowadays, powerful detection systems, which the learning procedure of them is black box and is not available, have been widely used to classify data. However, the understandability of the acquired knowledge from these detection systems can significantly help operator in carrying out classification performance with high accuracy and precision. Hence, knowledge acquisition in a form of fuzzy rule set is an important issue in the image processing that causes to comprehend the classification methods appropriately and to improve them subsequently. The purpose of this paper is proposing a method to extract fuzzy rules in IF-THEN form via an Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification LiDAR data and digital aerial images. Detection of building and tree in urban areas needs to determine some features to perform the detection procedure; because classification algorithms decide about pixel entity based on its feature vector. These features can make the object separation possible by the textural, the spectral, and the structural characteristics. Nowadays, by increasing the number of the active and passive sensors, it is possible to record the textural, the spectral, and the structural characteristics of objects in different wavelengths by various approaches. In this paper, some potentially features were generated, and then optimal features were selected using the genetic algorithm. Using the selected optimum features, an ANFIS was used to recognize the objects accurately. In this regard, at first, the prepared training data was utilized as inputs of grid partitioning algorithm and a Sugeno fuzzy inference system with one output was generated by determining the type and the number of input membership functions, as well as theS type of output membership functions. Then, the grid partitioning algorithm figured out the best state of the membership functions after investigating the whole of the possible states. Afterwards, the training and checking data were entered into the generated ANFIS and during the training procedure, the final classifier was concluded to detect buildings and trees. Finally, by proposing a different fuzzy-based method and using the selected training data, as well as the output membership functions of the proposed ANFIS, a set of effective fuzzy rules were extracted. The proposed method has three main steps. In the first step, the tuned premise parameters (after training process) of inputs training data of ANFIS were extracted according to the mean values of the membership functions. In the second step, firstly, based on the number of membership functions of each feature, the total number of feasible fuzzy rules was determined. Then, for each training data, the fired values for all rules were computed. The rule that had the most effect in the process was chosen as the fired fuzzy rule of each training data related to the desired object class. In the third step, the fuzzy rules which has the importance more than a specified threshold in the classification procedure were extracted. The extracted fuzzy rules were considered and analyzed logically regarding the feature layers, and the results show the high capability of the fuzzy-based proposed method in extracting rules from the objects detection procedure.

Some geological formations are more subject of weathering and alteration, so they are physically softer; in contrast, some other formations are more resistant against physical and chemical alterations, so they are harder and rocky on the ground surface. According to lack of geometric texture detection capability in usual optical images, the radar data must be undertaken. Because of the surface description capability of microwave remote sensing data, they are so much efficiently useful for morphological studies. Surface morphological modeling using Synthetic Aperture Radar (SAR) data requires topographic and micro-topographic surface model. Utilizing the ability of geometric pattern discrimination needs to relate dielectric and surface roughness parameters to radar signals back-scattering. Euclidean geometry is less capable in comparison to fractal geometry to describe natural phenomena. So far, some efforts are made to use fractal parameters in order to improve back-scattering model, but in none of them Euclidean geometry is not completely replaced by fractal geometry.Because of irregular nature of earth surface, geometry of the radar signal incidence on the earth surface is not Euclidean geometry and experimentally, fractal geometry describes it much better. Roughness of geological surfaces is an example of such natural phenomena. In literature, using fractal geometry in IEM has been performed by changing the computation process of some factors but, in all of such methods Euclidean geometry is the obvious rule of computation in IEM. In this paper, it is desired to replace the Euclidean geometry, basically by fractal geometry. Therefore, instead of conventional procedure of correlation length ( ) and rms-height ( ) calculation, two following equations are utilized: Where and are fractal surface parameters. According to adaptability of fractal theory with natural phenomena, it is supposed to generally have better results in IEM after having applied such improvements on the model input parameters.The methodology is implemented for PALSAR (ALOS satellite sensor) data analysis results of Anaran (between Dehloran and Ilam cities in Iran) geological formations. Field measurement of surface roughness using a total station and the data gathering performed on a grid of points. Thus, the digital elevation model (DEM) of the surface with sampling intervals smaller than the correlation length is formed.Surface roughness has been computed by IEM and get compared with field measurements on 20 selected pixels which show the most obvious improvement. In general, the behavior of the correlation function of the two polarization parameters are very close to each other. Although at sites 1 and 2 in some cases, lower standard deviation can be seen for Euclidean geometry, but mean standard deviation for fractal input parameters in IEM is considerably lower. Exponential ACF shows better for Site 1, and in contrary Gaussian ACF for Site 2 is more efficient; which confirms the fact that exponential ACF is suitable for soft surfaces and Gaussian one for rough ones.Due to irregular and fractal nature of the surface roughness, electromagnetic backscattering modeling of radar signals using fractal geometry calculates surface parameters closer to actual values. Gaussian correlation function is suitable for smooth and exponential correlation function is more appropriate for rough surfaces. The mean improvement in the use of fractal geometry for both polarizations hh and vv is about 50%. Comparison of three different sites with different levels of roughness provides similar results, and in particular, results improvement in areas with larger roughness parameters is more pronounced.

The dynamics of agriculture together with their in-depth influences on human and environmental conditions made the agricultural managers to look for new methods of information gathering. Such information must be accurate, effective and accessible on demand to lead the managers for better decisions. Agricultural changes are usually monitored in voluminous spatial-temporal databases. Spatial-temporal analysis of agricultural fields can confirm the changes, and its type to provide useful statistics. One way to collect such statistics is through spatiotemporal reasoning. The process of identifying differences in the state of a phenomenon by observing it at different times is called change detection. In remote sensing, change detection is done by using satellite images that are taken in different time epochs. The images are compared based on the corresponding pixel values. The results of this process are highly depended on the used methods as well as the interpretation strategies. Several techniques of change detection in remote sensing have been developed. Most of these methods are based on ground sampling. Control points are also used to evaluate the final results. Because of dynamic nature of corps, ground samples are only valid for one cultivation season and this process (ground sampling) should be repeated in every cultivation season. This, itself, increases the cost and the time required for change detection. In most of these methods interpretation of results is a complex approach that needs lots of experiences in remote sensing. Therefore, selecting appropriate method of change detection is important. Evaluation of studies about knowledge-based systems and their applications in Geomatics showed that, proper use of knowledge can increase the accuracy of existing methods. Therefore, in this research a knowledge-based change detection method was developed to detect changes of farms and to identify the type of changes. The proposed method has two main stages: 1) creating a spatial knowledge base and 2) inferencing stage. The knowledge base of this method includes three sets of spatial (geometric), temporal, and spectral information. These rules are achieved by analyzing of rotation history, time series of satellite imageries, farms maps and other facts which can increase the accuracy of change detection. Mugan plain in northwest of Iran was selected as the study area to test the proposed methodology. Rotation history of wheat farms and time series of Landsat 8 imageries were used to execute the test. Different sources affect multitemporal satellite-image datasets such as atmospheric effects, the sensor’s stability and responsiveness. Because of the importance of homogeneity of multitemporal satellite-image datasets, especially in vegetation change detection by remote sensing data, a relative radiometric normalization method was used. To achieve the temporal stability in series of images, this step is taken. In this process the radiometric properties of an image time series is adjusted to match that of a single reference image. Implementation of the method in wheat farm, proved to 86 percent accuracy in change detection and 80 percent of accuracy in type of changes. In this method, the type of changes is recognized through spatial knowledge-based, and no needs were found for using rendition. By removing mixed pixels, the proposed method resulted in an increase in accuracy of change detection and in the identification of the type of changes up to 95% and 90% respectively. Therefore, it is concluded that the results of this method are more accurate than that of Normalized Vegetation Index (NDVI) differencing and Post-Classification. NDVI resulted in 70% accuracy while Post-Classification Comparison has 81% percent accuracy. In addition, the proposed method reduced field work for data collecting.

Change detection has been one of the basic and crucial needs of management and exploitation of the environment and urban areas. Changes of the Urmia lake have been affected the life of the millions of the Iranian, Turkish and Azerbaijani people and their natural wildlife. Various methods and research studies have been developed to environmental change detection of the Urmia lake. The aim of this research study is to evaluate changes of Urmia Lake in the period of year 2006 until year 2011 by the Landsat 5 satellite images using the supervised fuzzy classifier. Therefore firstly, radiometric correction and image calibration have been applied to the both of the multi-year Landsat data. Then, bands number 4, 5 and 7 are selected as the references processing features according to the results of the previous works. Secondly, the multi-band differential features have been produced by subtracting the corresponding bands of the two Geo-referenced data of the mentioned years. Then two separate and multi-propose classification strategies have been applied to the produced feature space. Also obtained results are compared with the best outcome of the well-known SVM classification method.Firstly "two class fuzzy" classifier method on the differential features has been applied. The obtained results provided changed and not-changed classes. Achieved results for overall and average accuracy are 96.25 and 96.50 percent correspondingly. The reached results for "two class fuzzy" classifier are compared with the outcome of SVM classification and are shown the increasing about 17.04 and 10.6 percent for overall and average accuracy correspondingly.Because of the uncertainty, the word "changed" for some area has always been the big challenge. Sometimes the word "changed" can have many levels, which affects the management decisions. Also the changes can be divided as "little change", "mediocre change" and "more changed" classes, according to the different human attitudes. This kind of changes in the case study of the paper can be considered as "wet salt area", "dry salt area" and so on. Therefore secondly, the other fuzzy classifier is used to extraction of the not-changed, "little changed", "mediocre changed" and "more changed" classes. In the "four class fuzzy" classification method the training and test data are remained same as the previously mentioned "two class fuzzy" classification approach, while the defined fuzzy rules are alternated. The achieved classification results for the "four class fuzzy" method are shown the overall and average accuracy about 91.72 and 90.9 percent correspondingly. Moreover the class accuracy for the not-changed, little changed, mediocre changed and more changed classes are 96.14, 85.27, 94.70 and 85.88 percent respectively.The reached outcomes of the error matrix analysis are shown that the most correlation of the unchanged class is with the little changed class. Likewise the more correlation of the mediocre change class is with the more change class. The obtained result of the change detection for the "four class fuzzy" classification approach according to the human-oriented conceptual of relative changes in a phenomenon has the higher conceptual value.

The study of land use change is essential due to its significant effects on the environment and human life. Land use change modelers have mostly focused on the binary methods (e.g. urban and non-urban) rather than multiple land use changes methods. Also, most of the models used for modeling of land use changes are global parametric models (e.g. artificial neural network) and local non-parametric models (e.g. Multivariate Adaptive Regression Spline (MARS)) is rarely used to simulate multiple LUCs. Local models split the data into subsets and fit distinct models on each of the subsets. Non-parametric models do not have a fixed model structure or model structure is unknown before the modeling. On the other hand, global models perform modeling using all the available data. In addition, parametric models have a fixed structure before the modeling. In this paper, we applied one of the well-known data mining tools, called multivariate adaptive regression spline, as one of the local non-parametric models with geospatial information system and satellite images to simulate urban and agriculture land use changes for northern part of Iran including cities of Sari and Ghaem Shahr over a period of 22 years during 1992 and 2014. Landsat images are the core source for information extraction and modeling of land use change in this research. Landsat images of 1992 (TM) and 2014 (ETM+) were used for modeling the urban and agricultural land uses changes. The spatial predictors considered for urban and agriculture modeling in this area were distance to urban areas, distance to agriculture areas, distance to roads, distance to water, aspect, and slope in 1992. After the modeling, a sensitivity analysis was performed on the effective parameters of the land use changes. The results of the sensitivity analysis verified that the most important factors were distances from agricultural and urban areas as well as elevation. To assess the model performance, the receiver operating characteristics (ROC) and total operating characteristics (TOC) were used. Considering multiple thresholds, ROC reveals how strong each threshold of the generated index is in diagnosing either presence or absence of a characteristic which results in a two by two contingency table without informing the size of each entries. While preserving the important information revealing by ROC, TOC gives size information of each entry. The area under the receiver operating characteristics curve for urban and agricultural land uses were 65% and 61.01%, respectively. Also, we have labeled thresholds for 0.67 and 0.40 in total operating characteristic curves for agriculture and urban gain to show four entries in the two-by-two contingency tables, respectively. These thresholds represent the probability of land use change for pixels in the suitability maps. According to the results, the percent of observations that are reference change and have been diagnosed as change by the model are equals to 36.8% and 67.06% for these thresholds, respectively.

Nowadays, due to widespread use of maps and geospatial information systems in different organizations, different data layers collection methods for the preparation of required spatial data information, is of great importance. On the other hand doing in parallel in the preparation of spatial data and its storage in disparate sources often cause heavy costs. This has caused spatial infrastructure projects and clearing houses for communication between providers and the applicant.In this case the spatial data infrastructure is a communication network between the databases, systems and services, applications, technologies and policies that in different standard levels allows user access to data. As well as the clearing house in spatial information technology means decentralized system of information that each of information provides a description of spatial data. Although different methods of distributed programming on the network, such as a code on demand, client server and Web services are provided , for distributed computing and creating clearing house, but available methods when increasing the volume of requests and information faced with problems. Increasing network traffic, creating bottlenecks and delays in network, problem in computing on client-side and the need for special plug-ins, reducing network security and access to intelligence information, the possibility of disseminating information and lack of commercial use, reducing network power and other problems are of weaknesses of the common methods of distributed computing.By investigating the mobile agent it can be present to achieve the required objectives of the clearing houses and reducing problems of existing methods. Mobile agent provide a strong alternative and pattern for network and distributed computing which has a clear difference with the current common methodologies and indicators such as, code-on-demand, client-server and Web service method, which led to the replacement of use of mobile agent with current distributed computing methods as well as the use of this system, along with methods to reduce the existing problems. In artificial intelligence the agent is anything that can understand their environment through sensors and relationships and environment is also react by defined incentives.In this research mobile agent with autonomous ability to move and make decision on network is used to create a clearing houses to achieve the maps and spatial information needed in related organization databases and avoid spending high costs to mapping and doing in parallel. In implementing this project a clearing house has been created based on the use of mobile agents in order to meet legal and real users’ needs to access spatial data stored locally across multiple computer systems. For this purpose 4 database related to organizations responsible for mapping including available metadata and maps were considered. Accordingly using presented in this study clearing house have characteristics of mobile agents such as network load reduction, stability and resilience and ability to work in heterogeneous environments. Practical implementation example for a SDI clearing house confirms above mentioned capabilities.

By using the ascending and descending Envisat satellite images and also Multiple-Aperture Interferometry (MAI) and Conventional Interferometry techniques, we have calculated the co-seismic deformations on the satellite line of sight and on the azimuth track for the BAM earthquake in 2003. Also the three Orthogonal components of the displacement field from this geodetic measurements were obtained. In order for calculating the fault geometry and the slip distribution on the fault plane then, we inverted the components by using Genetic Optimization Method and also the Analytical Model (Okada Elastic Half-Space). The maximum of the slip was about 2.5 meters along with the 30 K.ms BAM fault on the approximate depth of 4~5 K.ms and by inverting, we have estimated seismic moment (M0), N.m 1018´7.6 that indicates a shock on 6.5 Mw scale. By inverting and using the Bootstrap Statistical Method we have also estimated the 68% confidence interval for model parameters.

In recent years, the techniques of surface representation have been changed and three-dimensional models have been replaced with two-dimensional maps. Airborne laser scanner as a powerful system has been known in remote sensing as a valuable source for 3D data acquisition from the Earth’s surface which can mainly be employed for 3D reconstruction and modeling. 3D reconstruction of buildings as an important element of 3D city models, based on LiDAR point clouds has been considered in this study. A new non-parametric method is proposed for generation of 3D model of buildings with flat and tilted roof. The approach comprises of four steps for 3D building reconstruction as: (A) Simultaneous building extraction and segmentation, (B) Edge detection, (C) Line approximation, and (D) 3D modeling. In step (A) a multi-agent method is proposed for extraction of buildings from LiDAR point clouds and segmentation of roof points at the same time. In this method five criteria such as height values, number of returned pulses, length, normal vector direction based on Constrained Delaunay Triangulation, and area are utilized. Next, in step (B) the edge points of roof segments are detected. Points of triangles having no neighboring triangles are extracted as primary edge points. In the extraction process, noises, external objects, and tree points on the roofs are eliminated. It is an advantage of the proposed method, however it leads to create the undesired edge points. There is the same problem regarding to segments which contain overlap with each other (like flat building). These undesired edge points as internal points are known and must be removed. In this paper, a method named “Grid Erosion” is employed for removing these internal points and therefore finding real edge points. After detecting the final edge points, a RANSAC-based algorithm is employed to approximate building lines in step (C). RANSAC is a powerful technique in line fitting and in comparison with general least square method, especially with noisy data, it provides robust results. In order to reduce the sensitivity of RANSAC to select parameters and no need for heavy post-processing, edge points are grouped by considering the angle between two consecutive connecting convex points. After classification of edge points, a RANSAC algorithm is separately applied on each classified edge-points group to produce primary lines. The regularization constraints should be applied on primary lines to generate the final lines. Finally, by modeling of the roofs and walls, 3D buildings model is reconstructed in step (D). The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. Building roof model is manually digitized from LiDAR point clouds and compared with building roof models that reconstructed using proposed method. In model reconstruction, the dominant errors are close to 30 cm which is calculated in horizontal distance. The main advantage of this method is its capability for segmentation and reconstruction of flat buildings containing parallel roof structures even with very small height differences (e.g. 10 cm). The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models.

In this study, we used an object-oriented method for merging pixel-based classification and image segments to get an optimal classification result. Urban land-cover classification is one of the important applications in polarimetric SAR remote-sensing images. Because of the nature of PolSAR images, many features can be extracted and used for classification. To achieve classification accuracy, optimal subset of features should be used. For this purpose, we used a class-based multiple classifier with SVM as a pixel-based classifier with class accuracy as a criterion in feature selection. Also we used random feature selection for create multi-classifiers. In addition, because of speckle noise in PolSAR images, pixel-based classification result may not be satisfactory. Thematic features used in image segmentation can be helpful to solve this problem. In general, the proposed method has three steps: feature selection, pixel-based classification, and polarimetric spatial classification. The pixel-based classification result is merged with a set of segments that are obtained from multi-resolution segmentation and the results are evaluated with overall accuracy and test pixels. The objectives of the study were to improve the accuracy of classification.Flowchart of the proposed algorithm presented as follows:The distinctive characteristic of synthetic aperture radar (SAR) sensors is the ability to provide a day-or-night, all-weather means of remote sensing. Recent SAR systems can produce high-resolution images of the land under the illumination of radar beams. SAR polarimetry is a technique that employs different polarization waves during transmission toward and reception from the Earth's surface and the resultant PolSAR images can be used in identification of different classes based on analyzing different polarization backscattering coefficients; by assigning pixels into different classes using a classification technique, the information contained in the SAR/PolSAR images can be interpreted.Classifier ensembles or multiple classifier systems (MCS) are methods in pattern recognition that are used for image classification; by combining different independent classifiers, MCS can improve classification accuracy in comparison with a single classifier. There are different methods for creating such an ensemble. These methods include modifying the training samples (e.g. bagging [1] and boosting [2]), manipulating the input features (the input feature space is divided into multiple subspaces [3]), and manipulating the output classes (multi-class problem is decomposed into two multiple class problems, e.g. the error correcting output code [3]). After creating an ensemble of classifiers, a decision fusion is used to combine the outputs of the classifiers. Several fusion algorithms have been developed and employed in the literature like majority voting, fuzzy integral, weighted summation, consensus, mixed neural network, and hierarchical classifier system [4], [5]. Class-based feature selection (CBFS) is a method that chooses features for each class separately to create a multiple classifier with manipulating input features. We used this method for pixel-based classification, and then fused single classifiers in two different ways described in the next section.Experimental results showed that the overall accuracy of the proposed method (90.07%) has improved compared with the single SVM classifier and pixel-based multiple SVM classifiers (83.61%).

Ever increasing expansion of wireless and mobile technologies, electronic microprocessors and various communication tools have led to progress the Context-aware computing domain. Geospatial Information System (GIS) is one of the primary systems which utilize such technologies. The main challenge in context-aware GISs is the modeling of relevancy between the user and his/her related contexts. Space and time are two dominant factors to detect the relevant contexts and the other types of relevancies are dependent on spatial and temporal relevancies (spatio-temporal relevancy). Therefore, this research is concentrated on modeling spatio-temporal relevancy in context-aware GISs. It seems that there is no report on modeling all types of spatial relevancies (such as topological, metric and directional relationships) regarding the movement and cognitive characteristics of the moving user in urban context-aware GISs. Regarding this framework, the main contribution of the paper is that the proposed model applies customized Fuzzy Multi Interval Algebra and Voronoi-based Continuous Range Query to introduce spatio-temporal relevant contexts according to their arrangement in space based on the position, cognition, velocity and direction of the user in fuzzy approach. The customization process is undertaken based on the Comprehensive Calculus principles reducing 169 Allen’s relations to 25 spatio-temporal relations. In this research, the user is a tourist who is supposed to be guided from a hotel (his/her origin), to a defined destination. The context-aware system guides him/her according to spatio-temporal relevant contexts. The assessment of the implemented model is done regarding to three parameters including accuracy, time performance and users satisfaction. Tehran Districts 3, 6 and 11 are selected as the study area. In order to test the accuracy parameters, the designed software is run in three different routes with two different velocities and three time durations in 100 iterations. Then the accuracy of the detection of the related contexts is tested by means of a binomial distribution with one-sided 95% confidence level, precision and recall factors. The results of implementation and evolution demonstrated the efficiency of the proposed model in an urban context-aware wayfinding system.

Ambient intelligence environment contains a large number of devices, services and applications which govern specific tasks to assist end-users. Each service domain has itself policy and managing such environments needs policies to determine suitable behavior to certain events. Policies are the way of specifying and influencing management behavior within a system, without coding the behavior into the manager agents. Typically, policy-based management systems use some form of authorization and obligation policies to guide system behavior. Obligation policies specify what actions an entity must or must not perform on the occurrence of an event. Whereas, authorization policies specify what actions an entity can or cannot perform on the occurrence of an event.Increasing ambient services may increase the number of users in joint areas of several domains simultaneously. These individuals will encounter unasked services which are sent by different domains in different policy patterns. In such conditions the role of policy conflict resolution and also service sending management is very important. Figure 1 shows two nested service domains with two different policies. The users locating in overlapped area are influenced by P1 and P2 services simultaneously. Consequently, conflicts will appear.

Imaging spectroscopy, also known as hyperspectral imaging, is concerned with the measurement, analysis, and interpretation of spectra acquired from either a given scene or a specific object at a short, medium, or long distance by a satellite sensor over the visible to infrared and sometime thermal spectral regions. The recent developments in spatial, spectral and radiometric resolution of hyperspectral images have stimulated new methodologies for land cover and land use classification. There are two major approaches for classification of hyperspectral images: the spectral or pixel-based and the spectral-spatial or object-based approaches. While the pixel-based techniques use only the spectral information of the pixels, the spectral-spatial frameworks employ both spectral characteristics and spatial context of the pixels. The pixel-based classification methods are often unable to accurately differentiate between some classes with high spectral similarity. This is mainly because they employ only the spectral information in order to identify different land covers. Consequently, methods that can exploit the spatial information are essential for more accurate classification results.Among the various methods for extracting spatial information, segmentation techniques are the powerful tools for defining the spatial dependences among the pixels and finding the homogeneous regions in the image. An alternative way to achieve the accurate segmentations of image is marker-controlled segmentation. The idea behind this approach is selecting of one or several pixels for every spatial object as the seed or a marker of the corresponding region. The marker-based segmentation significantly reduced the over-segmentation problem and led to better accuracy rate. Recently, an effective approach to spectral-spatial classification of hyperspectral images has been proposed based on Minimum Spanning Forest (MSF) grown from automatically selected markers using Support Vector Machines (SVM) classification. In this framework, a connected components labelling is applied on the classification map. Then, if a region is large enough, its marker is determined as the P% of pixels within this region with the highest probability estimates. Otherwise, it should lead to a marker only if it is very reliable. A potential marker is formed by pixels with estimated probability higher than a defined threshold.This paper aims at improving this approach by reducing the spatial dimensions of hyperspectral images. The proposed approach are evaluated the dimension reduction of hyperspectral image before and after marker selection process in MSF using genetic algorithm. The genetic algorithm is a general adaptive optimization search method based on a direct analogy to Darwinian natural selection and genetics in biological systems. It starts from an initial population which is composed of a set of possible solutions called individuals (chromosomes), and then evaluates the quality of each individual based on a fitness function. We use the Kappa coefficient accuracy parameter of SVM classification obtained from the training samples subset as the fitness function. Three benchmark hyperspectral datasets are used for evaluation: the Pavia dataset, the Telops dataset and the Indian Pines dataset. Experimental results show the superiority of using genetic algorithm before selecting markers in Pavia and Telops datasets. In Indian Pines dataset, the classification accuracy was increased with reduced dimensions both before and after the marker selection and concurrently.

Proper Site Selection of effective buildings in rescue (temporary housing) is necessary for long-term welfare of earthquake affected people. To select a suitable for the temporary accommodation of the population affected by natural disasters have always been considered and organization responsible for managing the crisis. Find a place to help citizens usually occurs by temporary help agencies without regard to standards and according to the personal experiences after an accident. Its clear that to choose an inappropriate place to house survivors is worse than the first place. This research proposes a model for appropriate and systematic site selection for temporary shelters, before an earthquake, using a Multi Criteria Decision Making (MADM) and Geographical Information System (GIS). A GIS-based system uses electronic mapping technology in producing interactive multi-layer maps so that queries are set to find optimal solutions for problems. It combines spatial and non-spatial data to construct visualized information that can be easily analyzed by decision makers.In this research, The district 8 of Isfahan municipality have been selected and studied based on three criteria; Population, aging tissue and culture. In this study we identify effective measures to locate temporary housing that has been selected from information relevant to the investigation according to the study area and data accessible. We categorized all the mentioned criteria in five main categories. Safety, The suitability of the land, Community and neighborhood, Availability and population are the main categories. A total of twenty-eight were considered criteria for selecting the suitable location. Based on the analytical methodology after using the criteria, weight matrix of paired comparisons the expert opinions have been obtained. Care centers and the sale of fuel, with the weight of 15.2%, is the most important criterion in this analysis. The remaining criteria have a weight range from 2% to 9.7%.Data associated with each of the layers by the appropriate phase, were fuzzy. Using hierarchical analysis and ArcGIS software, production levels for each criterion according to the specified weight, joined together, the output of the zoning map of the study area is temporary housing for injured owes. After that the value of 13 parks as a place for temporary housing calculated. In addition to the analytic hierarchy process for temporary housing location were performed using Vikor and Topsis. According to the ranking of the different ways, that the arithmetic mean was the first of these Borda and Copeland were then re-rating methods and ultimately to obtain the final ranking of the arithmetic mean of the three methods were different. Based on the analysis of the results of the comparison methods was determined that Vikor was the best and the closest final ranking and Topsis and AHP methods were in the next places. According to the final ranking Golestan park, North Noosh park and South Noosh park are the best places in temporary housing in the district. In addition, a parish map of the district was prepared. The map shows that the parishs of Bahramabad, Kesareh and Ferdovan respectively are the top ranking.

The number of high resolution space imageries in photogrammetry and remote sensing society is growing fast. Although these images provide rich data, the lack of sensor calibration information and ephemeris data does not allow the users to apply precise physical models to establish the functional relationship between image space and object space. As an alternative solution, some generalized models such as global polynomials have been developed and used. This paper presents a hybrid method based on using imperialistic competitive algorithm (ICA) to find the best terms of global polynomials. The method was carried out for geometric correction of two different datasets, an IKONOS Geo-image and a SPOT image, with different number of ground control points (GCPs) and independent check points (ICPs). Results showed the success of achieving sub-pixel accuracies (0.2) for IKONOS and 2.5 pixels for SPOT image. The method was able to successfully handle over-optimization as it produces lower RMSEs compared to conventional approach. Also, the proposed method required much less time in comparison to other optimization algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).

In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is the detection and elimination of outliers, which may appear to be inconsistent with the remainder of the observations or the model. Outlier detection is thus a primary step in many geodetic applications. There are various methods in handling the outlying observations among which a sequential data snooping procedure, known as Detection, Identification and Adaptation (DIA) algorithm, is employed in the present contribution. An efficient data snooping procedure is based on the Baarda’s theory in which blunders are detected element-wise and the model is adopted in an iterative manner. This method may become computationally expensive when there exists a large number of blunders in the observations. An attempt is made to optimize this commonly used method for outlier detection. The optimization is performed to improve the computational time and complexity of the conventional method. An equivalent formulation is thus presented in order to simplify the elimination of outliers from an estimation set-up in a linear model. The method becomes more efficient when there is a large number of model parameters involved in the inversion. In the conventional method this leads to a large normal matrix to be inverted in a consecutive manner. Based on the recursive least squares method, the normal matrix inversion is avoided in the presented algorithm. The accuracy and performance of the proposed formulation is validated based on the results of two real data sets. The application of this formulation has no numerical impact on the final result and it is identical to the conventional outlier elimination. The method is also tested in a simulation case to investigate the accuracy of the outlier detection method in critical cases when large amount of the data is contaminated. In the application considered, it is shown that the proposed algorithm is faster than the conventional method by at least a factor of 3. The method becomes faster when the number of observations and parameters increases.