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

作者：Xiaogang Ning, Hanchao Zhang, Ruiqian Zhang, Xiao Huang · 发表于：ISPRS Journal of Photogrammetry and Remote Sensing · 年份：2023 · DOI：10.1016/j.isprsjprs.2023.11.023 · 被引用次数：75 · 研究领域：Remote-Sensing Image Classification、Land Use and Ecosystem Services、Remote Sensing and Land Use

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 Pro...