Adaptive Local Structure Consistency-Based Heterogeneous Remote Sensing Change Detection
Lin Lei, Yuli Sun, Gangyao Kuang
Abstract
Change detection (CD) of heterogeneous remote sensing images is a challenging topic, which plays an important role in natural disaster emergency response. Due to the different imaging mechanisms of heterogeneous sensors, it is hard to directly compare the images. To address this challenge, we explore an unsupervised CD method based on adaptive local structure consistency (ALSC) between heterogeneous images in this letter, which constructs an adaptive graph representing the local structure for each patch in one image domain and then projects this graph to the other image domain to measure the change level. This local structure consistency exploits the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. To avoid heterogeneous data confusion, the pixelwise change image is calculated in the same image domain by graph projection. By comparing with some state-of-the-art methods, the experimental results show the effectiveness of the proposed ALSC-based CD method.