Hybrid Change Detection Based on ISFA for High-Resolution Imagery

Hybrid change detection (HCD) for high-resolution imagery usually adopt decision-level method and rely on artificial design. To address this issue, we propose a novel feature-level fusion strategy for HCD based on iterative slow feature analysis (ISFA). First, objects are obtained by multiresolution segmentation of bi-temporal images respectively, and corresponding feature sets are constructed through stacking pixel- and object-level spectral features. Then, slow feature analysis (SFA) is used for transforming the feature sets into a new feature space at the first time. And iteration method with variable weights is introduced to get the last slow feature fusion map, where the changed pixels and unchanged pixels can be separated more easily. At last, K-means cluster is adopted to separate changed area and unchanged area automatically and generate final change result. Experiments were conducted on bi-temporal multi-spectral images, demonstrating the good performance of the proposed approach.