Change detection of multisource remote sensing images: a review

Abstract

Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring, territorial planning, and disaster assessment. With the abundance of data collected by satellite, aircraft, and unmanned aerial vehicles, the utilization of multisource RS image CD (RSICD) enables the efficient acquisition of ground object change information and timely updates to existing databases. Although CD techniques have been developed and successfully applied for approximately six decades, a systematic and comprehensive review that addresses emerging trends, including multisource, data-driven, and large-scale artificial intelligence (AI) models, is lacking. Therefore, first, the development process of RSICD was reviewed. Second, the characteristics of multisource RS images were analyzed, and all publicly available RSICD data that we could gather were collected and organized. Third, RSICD methods were systematically classified and summarized on the basis of the detection framework, detection granularity, and data sources. Fourth, the suitability of specific data and CD methods for diverse applications and tasks was assessed. Finally, challenges, opportunities, and future directions for RSICD were discussed within the context of high-resolution imagery, multisource data, and large-scale AI models. This review can help researchers better understand this field, shed light on this topic, and inspire further RSICD research efforts.

Keywords

• Change detection
• remote sensing
• multisource images
• image processing
• review