Adaptive Local Structure Consistency based Heterogeneous Remote Sensing Change Detection
This addresses a challenging issue in remote sensing for emergency disaster response, but it appears incremental as it builds on existing graph-based approaches for heterogeneous data.
The paper tackles the problem of change detection in heterogeneous remote sensing images by proposing an unsupervised method based on adaptive local structure consistency, which projects graphs between image domains to measure changes, and experiments show it outperforms state-of-the-art methods.
Change detection of heterogeneous remote sensing images is an important and challenging topic in remote sensing for emergency situation resulting from nature disaster. Due to the different imaging mechanisms of heterogeneous sensors, it is difficult to directly compare the images. To address this challenge, we explore an unsupervised change detection 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 the leakage of heterogeneous data, the pixelwise change image is calculated in the same image domain by graph projection. Experiment results demonstrate the effectiveness of the proposed ALSC based change detection method by comparing with some state-of-the-art methods.