Change Detection Approach to Sar and Optical Image Integration

In order to overcome the insufficiency of single remote sensing data source during information extraction, to make use of the complementary characteristics of SAR data and optical imagery, and to facilitate better monitoring and evaluation of resources and ecological environment, this paper develops the idea and presents the approach to land use/cover change detection by different temporal SAR and optical image integration. Aiming at the different imaging mechanisms and information characteristics of optical imagery and SAR data, this paper employs the object-oriented image analysis technique for high accuracy information extraction from high-resolution optical imagery; it proposes a multi-scale and multi-texture feature fusion method based on SVM for information extraction from single-band and single-polarization SAR data by employing the multi-scale textural analysis technique and the fractal analysis technique; it then integrates individually extracted information of different temporal for change information extraction by applying a series of decision rules; it also develops the method for analysis and evaluation of uncertainty of the change detection result at the scale of pixels using the extended probability vector. Data adopted in this research are different temporal SPOT5 image and Radarsat-1 SAR data. Experimental results prove the correctness, reliability and effectiveness of the methods proposed in this paper.