Deep Learning-Based Change Detection in Remote Sensing: A Comprehensive Review

Abstract

Change detection in remote sensing (RS) images typically involves processing and analyzing RS images of the same geographic location captured at different times to identify changes. In recent years, deep learning has seen increased application in the field of RS, particularly in the context of change detection in RS images. Compared to conventional approaches, deep learning-based methods can automatically identify subtle changes in high-resolution RS images with greater accuracy and efficiency. This article provides an overview of deep learning-based change detection research from a variety of perspectives, including data samples, algorithms, computational power, and commercially driven developments. First, this article compiles datasets for RS change detection, including SAR, multispectral, hyperspectral, and multimodal images. Second, it introduces the process and framework of deep learning-based change detection, along with mainstream deep learning networks employed for image feature extraction. In addition, change detection is described in terms of different detection granularity sizes and different detection methods. Furthermore, this article provides the first analysis of deep learning-based RS change detection from the perspective of computational power and commercially driven developments. This article also discusses the current major challenges and future directions in the field of deep learning change detection. Deep learning-based methods need to overcome the problem of undesirable samples, integrate multimodal RS images, focus on the extraction of multiple change information, and advance the development of foundation models for change detection.