A Comprehensive Review of Remote Sensing Image Change Detection Methods
DOI:
https://doi.org/10.54097/5pkw3c69Keywords:
Remote sensing, change detection, deep learning, Siamese network, TransformerAbstract
Remote sensing image change detection aims to identify spatiotemporal changes in land cover, land use, and ground-object structures by comparing remote sensing images acquired over the same area at different times. It is an important research topic at the intersection of remote sensing image processing, geographic information science, and computer vision. With the rapid accumulation of high-resolution, multi-source, and multi-temporal remote sensing data, change detection has been widely applied to urban expansion monitoring, land-use surveys, disaster damage assessment, and ecological environmental protection. This review focuses on remote sensing image change detection. It summarizes the general workflow, outlines the principles and limitations of traditional methods, and systematically reviews deep learning-based change detection methods. In addition, mainstream public datasets and commonly used evaluation metrics are summarized. The core challenges faced by current research are analyzed from the perspectives of data quality, data resources, and methodological design, including image registration errors, pseudo-change interference, and the high cost of annotation. Finally, future research directions are discussed to provide references for method development, model optimization, and practical applications in remote sensing image change detection.
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