深度学习遥感变化检测综述：典型算法及发展趋势

Bi-temporal remote sensing image change detection stands as a prevalent direction in the realm of intellectual interpretation and applied research of remote sensing imagery. It aims to acquire information regarding changes in land cover types or geophysical attributes within a monitored area over a specified time span, according to practical application requirements. Over the past few years, remote sensing image change detection techniques have undergone a rapid evolution and upgrading, fueled by the synergetic forces of the ever-growing remote sensing big data (especially the proliferation of very-high-resolution remote sensing images) and the revolutionary advancements in deep learning. In this paper, we delve into and analyze the existing popular algorithms for common change detection tasks using bi-temporal very-high-resolution remote sensing images, encompassing binary change detection that aims to identify the presence or absence of changes, semantic change detection that delves deeper into the semantic categories of the changed areas, building damage assessment that applies to natural disasters, and change captioning that focuses on generating meaningful descriptions of the detected changes using natural language. Finally, we present an outlook on the pivotal research trends in remote sensing image change detection and highlight lingering open issues under the current developmental trajectory, with the intention of offering some valuable insights and perspectives for future research endeavors in this field. Through this review, we raise two observations as follows. 1) Deep learning solutions for the common change detection tasks in remote sensing have been continuously evolving, with many innovative algorithms proposed; 2) The advanced capabilities of current vision and language foundation models have subtly permeated into this field. In fact, the remote sensing domain itself has witnessed a highly encouraging development trend of foundation models. On this basis, remote sensing change detection tends to embark on new development opportunities, and would face a reshaping of its technological landscape in the future. Though, researchers have to spend efforts in developing specialized solutions to (1) enhance the reliability of change detection in complex scenes. At present, even for the relatively mature fully supervised binary change detection, the detection results often show a complete loss of changed entities when faced with some complex scenarios. In semantic change detection, there may even be a phenomenon where the segmented changed semantics are not consistent with the binary change detection results. (2) reduce the dependence on bitemporal image registration. This requires new algorithms to be developed to adaptively handle small misalignments and deformations in the spatial positions of corresponding objects in bi-temporal images and complete change detection in this case. (3) improve the practicality of multi-modal change detection. This may ask for a comprehensive framework that integrates multi-modal information extraction, feature registration and fusion, and change detection. Meanwhile, semi-supervised or weakly supervised learning methods are expected to become a research focus due to the annotation difficulty of heterogeneous remote sensing data. Besides, it is also believed that the synergy between image modality and language modality has broad prospects for future research in remote sensing change detection, owing to the increasing advancement of large language models as well as vision and remote sensing foundation models.