Improved Robust Kernel Subspace for Object-Based Registration and Change Detection

Postclassification comparison is an approach to detect changes in remote sensing images that have strongly inhomogeneous scenes. It is a challenging task to register pre- and postevent scenarios, because variform classifications may induce an inadequate number of homologous points to be used as tie points. In this letter, we show how the variform objects can be precisely registered using their robust kernel subspace. There are two primary contributions in our work. First, a robust kernel subspace analysis method is proposed to capture the common patterns of the variform objects. Second, a registration method based on the common patterns and their preimage are derived. The power of the proposed approach is demonstrated by two real applications: one for lake monitoring in the Jiayu region and the other for damage mapping of earthquake-induced barrier lake at Tangjiashan. The results show that the proposed method is effective in structural pattern analysis and object registration.