Change Detection of Polarimetric SAR Images Based on the Integration of Improved Watershed and MRF Segmentation Approaches

Land cover and land use change detection is one of the most important applications of polarimetric synthetic aperture radar (PolSAR) data. In a pixel-based method, the speckle effects and the lack of consideration for spatial information produce false alarms. Therefore, an object-based image analysis has been used to overcome these weaknesses. This paper proposes a novel hybrid image segmentation algorithm for improving the accuracy of land cover change detection. This method consists of three steps. First, the segmentation of two PolSAR images by integrating segmentation techniques, namely, region-based improved watershed and an edge-based coupled Markov random field (MRF). Second, the selection of the optimal ratios of polarimetric features based on a genetic algorithm and Jefferies-Matusita distance criteria. Third, the binary classification of image objects using the ratio of mean pixel values of the corresponding image objects. Compared with the conventional watershed and multiresolution segmentation methods, the improved watershed reduces the speckle effects in PolSAR images and avoids the oversegmentation problem. The use of coupled MRF detects the most accurate edge positions and improves the hybrid segmentation approach. The proposed change detection method, as applied on unmanned aerial vehicle synthetic aperture full polarimetric images, demonstrates an overall accuracy of 90.14% and a kappa coefficient of 0.80. Compared with the pixel-based change detection method based on a support vector machine classifier for the selected subspace of ratios of polarimetric features, the proposed method improved the overall accuracy by 8.72% and the kappa coefficient by 0.18.