Remote sensing image change detection using deep learning techniques: a comprehensive survey

Abstract In recent years, deep learning algorithms have been widely regarded as the preferred method for remote sensing image analysis and have been successfully applied in the field of change detection. However, currently only a few comprehensive review papers on deep learning based remote sensing image change detection methods are available. To address the above issues, this study provides the current research status and development trends of remote sensing image change detection and analysis based on deep learning. Firstly, considering the differences in data volume and in data characteristics between different data sources, unlike previous reviews that only focus on the change detection problem of a certain type of remote sensing data, this review outlines the various data types involved in remote sensing image change detection, mainly including very high-resolution data, hyperspectral data, synthetic aperture radar data, and heterogeneous data. Secondly, unlike previous reviews that only introduce deep learning methods as a category of methods, this review comprehensively summarizes the research progress in remote sensing image change detection from three aspects: supervised deep learning, semi-supervised deep learning, and unsupervised deep learning, and explores the advantages and limitations of these methods. On this basis, we also propose five interesting research directions to promote further development in this field, including data privacy protection, multi-modality semantic-level change detection, lightweight models, change detection assisted by foundational models, as well as brain-inspired and vision-language models for change detection. This study will help deepen our understanding of deep learning in change detection in multiple ways and lay the foundation for future research.