Multi-stage progressive change detection on high resolution remote sensing imagery

Change detection in remote sensing images, especially optical high and very high resolution images, is a pivotal technique that enables efficient identification of Earth observation alterations, with widespread applications including land use analysis, urban planning, environmental monitoring, and disaster mapping. Of these applications, change detection is predominantly employed in urbanization studies to monitor developmental changes in construction land. However, the pronounced class imbalance and variances between image domains present formidable challenges to change detection implementations. In response to these issues, we introduce the concept of reverse change discovery, redefining the primary goal of model learning for change detection in optical remote sensing images as stable invariant region detection. In line with this concept, we present a novel Multi-Stage Progressive Change Detection Network (MSP-CD) specifically designed for urbanization change detection, which integrates invariant detection, knowledge distillation, and a coarse-to-fine change detection structure. To validate the effectiveness and robustness of the proposed method, we conducted experiments using two widely recognized public datasets, namely the Lebedev-CD and Levir-CD datasets. The MSP-CD network demonstrated state-of-the-art performance across these datasets. Additionally, we curated an application-specific change detection dataset (JS-CD dataset) using remote sensing images from Jiangsu Province in China, which differs from public datasets in terms of annotations, to further validate the MSP-CD network's performance. A case study on regional urbanization development in Yangzhou city, conducted using the proposed MSP-CD network, further attests to the applicability of our proposed change detection framework.