Papers by Farhad Samadzadegan
Fry analysis and weights of evidence were employed to study the spatial distribution and spatial ... more Fry analysis and weights of evidence were employed to study the spatial distribution and spatial association between known occurrences of geothermal resources and publicly available geoscience data sets at regional-scale. These analyses support a regional-scale conceptual model of geological, geochemical and geophysical interaction by calculating the optimum cutoff distance and weight of each evidence feature. Spatial association analysis indicated the geochemical and geophysical data play more important roles than geological data as evidence layers to explore geothermal resources. Integration of spatial evidential data indicates how these layers interacted to form the geothermal resources. Boolean index overlay, Boolean index overlay with OR operation, multi-class index overlay and fuzzy logic prediction models were applied and compared to construct prospective maps. Prediction rate estimator showed the fuzzy logic modeling resulted in the most reliable and accurate prediction with prediction rate about 26 in the high-favorite areas. (M.K. Moghaddam). volcanic rocks, though the substantial issue is that the existence of tectonic elements and high heat flow are more important than rock type .
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013
Three-dimensional building models are important in various applications such as disaster manageme... more Three-dimensional building models are important in various applications such as disaster management and urban planning. In this paper a method based on fusion of LiDAR point cloud and aerial image data sources has been proposed. Firstly using 2D map, the point set relevant to each building separated from the overall LiDAR point cloud. In the next step, the mean shift clustering algorithm applied to the points of different buildings in the feature space. Finally the segmentation stage ended with the separation of parallel and coplanar segments. Then using the adjacency matrix, adjacent segments are intersected and inner vertices are determined. In the other space, the area of any building cropped in the image space and the mean shift algorithm applied to it. Then, the lines of roof's outline edge extracted by the Hough transform algorithm and the points obtained from the intersection of these lines transformed to the ground space. Finally, by integration of structural points of intersected adjacent facets and the transformed points from image space, reconstruction performed. In order to evaluate the efficiency of proposed method, buildings with different shapes and different level of complexity selected and the results of the 3D model reconstruction evaluated. The results showed credible efficiency of method for different buildings.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013
The interest in the joint use of remote sensing data from multiple sensors has been remarkably in... more The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. This is because a combined use is supposed to improve the results of classification tasks compared to single-data use. This paper addressed using of combination of hyperspectral and Light Detection And Ranging (LIDAR) data in classification field. This paper presents a new method based on the definition of a Multiple Classifier System on Hyperspectral and LIDAR data. In the first step, the proposed method applied some feature extraction strategies on LIDAR data to produce more information in this data set. After that in second step, Support Vector Machine (SVM) applied as a supervised classification strategy on LIDAR data and hyperspectal data separately. In third and final step of proposed method, a classifier fusion method used to fuse the classification results on hypersepctral and LIDAR data. For comparative purposes, results of classifier fusion compared to the results of single SVM classifiers on Hyperspectral and LIDAR data. Finally, the results obtained by the proposed classifier fusion system approach leads to higher classification accuracies compared to the single classifiers on hyperspectral and LIDAR data.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
ABSTRACT Supervised classification of hyperspectral image data using conventional statistical cla... more ABSTRACT Supervised classification of hyperspectral image data using conventional statistical classification methods is difficult because a sufficient number of training samples is often not available for the wide range of spectral bands. In addition, spectral bands are usually highly correlated and contain data redundancies because of the short spectral distance between the adjacent bands. To address these limitations, a multiple classifier system based on Adaptive Boosting (AdaBoost) is proposed and evaluated to classify hyperspectral data. In this method, the hyperspectral datasets are first split into several band clusters based on the similarities between the contiguous bands. In an AdaBoost classification system, the redundant and noninformative bands in each cluster are then removed using an optimal band selection technique. Next, a support vector machine (SVM) is applied to each refined cluster based on the classification results of previous clusters, and the results of these classifiers are fused using the weights obtained from the AdaBoost processing. Experimental results with standard hyperspectral datasets clearly demonstrate the superiority of the proposed algorithm with respect to both global and class accuracies, when compared to another ensemble classifiers such as simple majority voting and Naïve Bayes to combine decisions from each cluster, a standard SVM applied on the selected bands of entire datasets and on all the spectral bands. More specifically, the proposed method performs better than other approaches, especially in datasets which contain classes with greater complexity and fewer available training samples.
This paper, introduces a novel multiresolution method for automatic image registration with respe... more This paper, introduces a novel multiresolution method for automatic image registration with respect to image-to-object spaces based on key features consideration. The present approach is designed to be completely independent from the sensor type and any prior information on the exterior orientation. Moreover, in the proposed procedure, Genetic algorithm (GA) is used to match the corresponding features and fit the satellite image on the vector map by optimizing the transformation accuracy on checked and control points. The potential of the proposed method is evaluated using IKONOS imagery and corresponding digital vector map. During the time lapse between the generated map and image acquisition, considerable changes have also occurred in the city. This method has proved to be very efficient and reliable for automatic registration of satellite imageries based on digital maps.
Journal of Algorithms and Computation, May 4, 2013
LIDAR technology has become a standard tool for collecting 3D data from complex surface of 3D obj... more LIDAR technology has become a standard tool for collecting 3D data from complex surface of 3D object such as building and tree in urban area. There is a wide range of investigation in extracting different man made or natural objects from dense LIDAR data. Practice shows for extracting information from massive point in complex surface, some clustering processes are required for grouping LIDAR data. Referring to density and complexity of LIDAR data, conventional clustering methods are not appropriate enough. Recently several bio-inspired clustering techniques are proposed to overcome drawback of traditional methods. One of the main categories of these techniques which inspired by the social behavior of living organism is ant colony optimization. In this paper, three clustering algorithms based on ant colony optimization are proposed and their results compares with traditional k-means method. These techniques are based on Foraging behavior and Cemetery organization of ant colony. Foraging based clustering and cemetery organization algorithm results demonstrate considerable improvement in quality of clustering result. Referring to the ability of the ant colony optimization to perform local and global search simultaneously, these algorithms can find global optimum in clustering of dense and complex LIDAR data. The main advantage of cemetery organization algorithm is ability to work even when cluster number is unknown. By hybridization of foraging behavior and k-means, the quality of clustering result and processing time become much better than other techniques.
Automatic pose estimation remains to be one of the challenging problems in photogrammetry and com... more Automatic pose estimation remains to be one of the challenging problems in photogrammetry and computer vision. When other systems (e.g. GPS and INS) are inadequate, and real time precise positioning is required, image is a very high quality device for localization of vehicles. In this paper, we address a method for Vision Based Navigation (VBN) of Aerial Vehicles which is
Journal of the Indian Society of Remote Sensing, May 29, 2013
With recent technological advances in remote sensing sensors and systems, very highdimensional hy... more With recent technological advances in remote sensing sensors and systems, very highdimensional hyperspectral data are available for a better discrimination among different complex landcover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or 'curse of dimensionality' in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. 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, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015
Spectral similarities and spatial adjacencies between various kinds of objects, shadow, and occlu... more Spectral similarities and spatial adjacencies between various kinds of objects, shadow, and occluded areas behind high-rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in object recognition in urban areas. Using a knowledge base containing the contextual information together with the multiviews imagery may improve the object recognition results in such a situation. The proposed object recognition strategy in this paper has two main stages: single view and multiviews processes. In the single view process, defining region's properties for each of the segmented regions, the object-based image analysis (OBIA) is performed independently on the individual views. In the second stage, the classified objects of all views are fused together through a decision-level fusion based on the scene contextual information in order to refine the classification results. Sensory information, analyzing visibility maps, height, and the structural characteristics of the multiviews classified objects define the scene contextual information. Evaluation of the capabilities of the proposed context aware object recognition methodology is performed on two datasets: 1) multiangular Worldview-2 satellite images over Rio de Janeiro in Brazil and 2) multiviews digital modular camera (DMC) aerial images over a complex urban area in Germany. The obtained results represent that using the contextual information together with a decision-level fusion of multiviews, the object recognition difficulties and ambiguities are decreased and the overall accuracy and the kappa are gradually improved for both of the WorldView-2 and the DMC datasets.
The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possibl... more The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possible classification performance for recognition of different objects such as buildings, roads and trees .Extracting of road from newer Lidar data is one of the main challenge in photogrammetry and computer vision. Roads in Lidar data appear as homogenous area in same height. In this paper the idea is to combine classifiers with different error types by a learnable combiner which is aware of the classifiers' expertise, so that the variance of estimation errors are reduced and the overall classification of road accuracy is improved. In this paper we used Naïve Bayes and Weighted Voting Method as classifier fusion methods. The results quality was assessed for each classification method with the same validation set of pixels computing the confusion matrix. Experimental results show that the proposed model outperforms results with higher accuracy rather than single classifiers.
Three dimensional object recognition and extraction from Lidar and other airborne or space borne ... more Three dimensional object recognition and extraction from Lidar and other airborne or space borne data have been an area of major interest in photogrammetry for quite a long time. Therefore, many researchers have been trying to study and develop automatic or semi-automatic approaches for object extraction based on sensory data in urban areas. Lidar data have proved to be a promising data source for various mapping and 3D modeling of objects. But, according to the complicated relationships between objects in urban areas, especially buildings and trees, the performance of obtained results from ordinary object recognition algorithms based on Lidar data, is still dependent on several assumptions and simplifications. In this paper a multi-agent strategy has been proposed for automatic building and tree recognition based on the fusion of textural and spatial information extracted from Lidar range and intensity data. In this multi-agent methodology, two different groups of object recognition agents are defined for building and tree recognition in parallel and the algorithm has two different operational levels based on the types of contextual information. According to the difficulties in the field of building and tree detection based on the textural descriptors or spatial context, using the communicational behaviors and other capabilities of the multi-agent systems can be so useful in the field of 3D object recognition in dense urban areas. The evaluation of obtained results of the proposed methodology confirms the high capabilities of Lidar data and this multi-agent algorithm to decrease the conflicts in the field of building and tree recognition in parallel.
Photogrammetric Engineering and Remote Sensing, Mar 1, 2005
Abstract Generic sensor models (GSMs) are comprehensive mathemati-cal models by which different g... more Abstract Generic sensor models (GSMs) are comprehensive mathemati-cal models by which different geometric structures of satellite images could be modeled in order to establish the connection between image and object spaces. Nevertheless, as they are mathematical models, ...
Geo-referencing through rectification remains to be one of the challenging problems in remote sen... more Geo-referencing through rectification remains to be one of the challenging problems in remote sensing and various imagery applications such as image pose estimation, image to Object Registration and 3D map generation. Rigorous mathematical models with the aid of satellite ephemeris data can present the relationship between the image space and object space. With government funded satellites, access to calibration and ephemeris data allowed the development of these models. However, for commercial highresolution satellites, these data have been withheld from users, and therefore alternative empirical rectification models have been developed. In general, most of these models are based on the use of control points. Visibility and uniqueness of distinct control points in the input imagery limit use of this feature for robust Georeferencing procedure and provide a catalyst for the development of algorithms based on other image features. Recent advances in digital photogrammetry and Remote sensing mandate adopting higher-level primitives such as control linear features for replacing traditional control points. Linear features can be automatically extracted from the image space. On the other hand, object space control linear features can be obtained from an existing GIS layer containing 3D vector data such as road network, or from terrestrial Mobile Mapping Systems. In this paper, we present a new model named the Line Based Generic Model (LBGM), for Georeferencing of High Resolution Satellite imageries. The model has the flexibility to either solely use linear features or control point to define the image transformation parameters. As with other empirical models, the LBGM does not require any sensor calibration or satellite ephemeris data. Synthetic as well as real data have been used to check the validity and fidelity of the model, and experimental results proved the feasibility and robustness of LBGM approach, especially when compared to those obtained through traditional point based transformation models.
Abstract The complexities associated with the sensor placement for the purpose of extraction of 3... more Abstract The complexities associated with the sensor placement for the purpose of extraction of 3D coordinates of the objects have made the automatic solution of this problem rather impractical. These complexities are due to the fact that sensor positions, attitude and ...
With respect to the fact that nearly 60% of the world's population live in the cities and re... more With respect to the fact that nearly 60% of the world's population live in the cities and regarding the inevitable structural complexities as well as the cultural and economical complications in the cities, the intervention of a sophisticated information management systems has ...
Advances in Spatial Data Handling, 2002
Cellular automata models consist of a simulation environment represented by a gridded space (rast... more Cellular automata models consist of a simulation environment represented by a gridded space (raster), in which a set of transition rules determine the attribute of each given cell taking into account the attributes of cells in its vicinities. These models have been very successful in view of their operationality, simplicity and ability to embody logics-as well as mathematics-based transition rules in both theoretical and practical examples. Even in the simplest CA, complex global patterns can emerge directly from the application of local rules, and it is precisely this property of emergent complexity that makes CA so fascinating and their use so appealing.
ABSTRACT Data integration or fusion refers to the acquisition, processing and synergistic combina... more ABSTRACT Data integration or fusion refers to the acquisition, processing and synergistic combination of information provided by various source of data. The scope of this article is to describe three typical applications of data integration in photogrammetry and remote sensing. The first study case refers to the evaluation of the potential of different image fusion techniques on integration of two satellite images with different spatial and spectral resolution. The second one considers the problem of the object extraction in outdoor situations and the solution which proposed base on a feature level fusion. The third one presents the characteristics of a decision level fusion strategy for construction of an automatic system for detection of changes between available sensor information and corresponding digital vector map. Each study case presents also the results achieved by the proposed techniques applied to real data.
Network analysis in geospatial information system (GIS) provides strong decision support for user... more Network analysis in geospatial information system (GIS) provides strong decision support for users in searching optimal route, finding the nearest facility and determining the service area. Searching optimal path is an important advanced analysis function in GIS. In present GIS route finding modules, heuristic algorithms have been used to carry out its search strategy. Due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. This paper addresses the problem of selecting route to a given destination on an actual map under a static environment. The proposed solution uses a genetic algorithm (GA). A part of an arterial road is regarded as a virus. We generate a population of viruses in addition to a population of routes. A customized method based on a genetic algorithm has been proposed and successfully implemented in an area in the north-east of Tehran using the optimal combination of viruses.
Uploads
Papers by Farhad Samadzadegan