Image fusion for land cover change detection

Image fusion is an effective approach for enriching multi-source remotely sensed information. In order to compensate the insufficiency of single-source remote sensing data during the change detection process, and to combine the complementary features from different sensors, this article presents the results of different temporal synthetic aperture radar (SAR) and optical image fusion algorithms for land cover change detection. First, pixel-level image fusion is performed, and its applicability for change detection is assessed by a quantitative analysis method. Second, change detection at the decision-level is put forward, which comprises object-oriented image information extraction from high-resolution optical image, multi-texture feature and support vector machines (SVM)-based information extraction from single band and single polarisation SAR image, and hard-and soft-decision based change detection. Change detection uncertainty is also evaluated at the scale of pixel using the extended probability vector and probability entropy model. The imagery used in this image fusion research was SPOT5 and RADARSAT-1 SAR data.