Point Label Meets Remote Sensing Change Detection: A Consistency-Aligned Regional Growth Network

Abstract

The acquisition of a substantial volume of precisely dense pixel-annotated samples plays a crucial role in the effective training of deep learning-based change detection models. Nevertheless, in real-world scenarios, pairwise labeling of massive bitemporal remote sensing images is often laborious and time-consuming, resulting in the lack of labeled samples. In this article, we propose a novel point-based weakly supervised learning approach, called as the consistency-aligned regional growth network (CARGNet), for remote sensing change detection. Unlike pixel-level labels, point labels are easy to label and usually sparse, which leads to a lack of boundary information, making it difficult for the model to accurately capture the details of the changed objects. Therefore, learning directly from them may mislead the training of the network. To address these problems, we introduce a point-based changed regional growth (PCRG) module and consistency alignment (CA) constraint into CARGNet, which breaks the limitation of point labels in losing important target details. Specifically, our CARGNet contains two branches: a base decoder branch and an expanded decoder branch. First, we utilize the PCRG module to generate the expanded annotations from the point annotations. Then, the base decoder is supervised by the original point annotations, while the expanded decoder is supervised by the expanded annotations. Finally, the CA constraint is thereby achieved by minimizing the discrepancy between the predictions from both the base and the expanded decoders, which greatly improves the performance of the model. Experimental results on LEVIR-CD-Point and DSIFN-CD-Point datasets demonstrate that our proposed CARGNet can achieve highly competitive results compared with state-of-the-art fully-supervised methods. Code and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Wanderlust717/CARGNet</uri> .