SAR Image Change Detection Based On Spatially Nonstationary Analysis

SAR image Change detection, which aims at identifying changed areas occurred on the Earth's surface, is a process of making a direct comparison of a pair of SAR images acquired over the same geographical area at different times. With the development of SAR, SAR image change detection has been widely studied.This dissertation studies the SAR image change detection based on SAR image non-stationary analysis, given the satelliteborne SAR images acquired over the same geographical area at different times. The main contents of this dissertation are summarized as follows:1. A SAR image change detection algorithm based on SAR image non-stationary analysis and SVM model has been proposed in this paper. This algorithm divides the SAR images into different non-stationary regions by useing the SAR image non-stationary area dividing algorithm, and obtaines binary images of these non-stationary regions by using SVM model, then obtaines the SAR image change detection result by mergeing these binary images. Finally, experimental results on four sets of two temporal SAR images validate the effectiveness of the proposed SAR image change detection algorithm.2. A SAR image change detection algorithm based on SAR image non-stationary analysis and CRF model has been proposed in this paper. This algorithm is constructed by two parts: the unary potential and the pairwise potential. The unary potential is constructed by the support vector machine(SVM) using the texture features which can output the class conditional probability, SVM integrates various features into high-dimensional space, thus handling the non-linear problem and improving accuracy of the model, and the pairwise potential is modeled by the multilevel model which being utilized to regulate the interactions and capture the edge information.This algorithm has the superiorities of capturing the contextual information of the observed data and complex structures of images through extracting various features. Finally, experimental results on four sets of two temporal SAR images validate the effectiveness of the proposed SAR image change detection algorithm.3. A SAR image change detection algorithm based on SAR image non-stationary analysis and HCRF model has been proposed in this paper. This algorithm consists of three parts: the unary potential, the pairwise potential and the data term modeled by the statistics of the log-ratio image. The unary potential and the pairwise potential are constructed in the same way with the SAR image change detection algorithm based on SAR image non-stationary analysis and CRF model. Generalized Gamma distribution( G(38)D) is utilized to model the statistics of the intensity data in the log-ratio image. The parameters in this algorithm model are estimated using iterative conditional estimation algorithm, and the iterated conditional modes is used to inference this algorithm model in the process of change detection. Finally, experimental results on four sets of two-temporal SAR images validate the effectiveness of the proposed SAR image change detection algorithm.