Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values chang...Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.展开更多
In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to ...In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to extract building areas in ur- ban areas from very high resolution SAR images is presented. The results showed that this method has the ability to differentiate buildings from the complicated features in urban areas, which can be employed for land mapping and provides support for relief operations.展开更多
文摘Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.
基金Supported by the National Key Technology R & D Program of China (No.2008BAK49B04)the Project of State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University
文摘In this paper, the textural characteristics of the buildings were quantified by using two texture descriptors, namely, Square Root Pair Difference （SRPD） and Gi ＊. Then, a novel method, based on SRPD and Gi ＊, to extract building areas in ur- ban areas from very high resolution SAR images is presented. The results showed that this method has the ability to differentiate buildings from the complicated features in urban areas, which can be employed for land mapping and provides support for relief operations.
