SWCD: Toward Accurate Change Detection via Similarity-Awareness Weakly Supervised Learning

Change detection (CD) is one of the prominent research topics in the fields of Earth science and remote sensing. Recently, an increasing number of deep learning-based CD methods have been developed. Most of the current CD methods require lots of pixel-level labels for supervised learning. However, annotating all the changed pixels in bitemporal images is both challenging and time-consuming. In this work, as a first attempt in the field of CD, we propose a novel CD framework, similarity-awareness weakly supervised CD (SWCD) to achieve accurate CD, which uses weakly supervised learning as an auxiliary task to guide the model in both semi-supervised and supervised learning. In the weakly supervised branch (WSB), we incorporate the concept of similarity and introduce similarity information into the supervised branch to guide pixel-level CD learning, thus enhancing feature continuity. Moreover, large kernel convolution attention is introduced to enhance multiscale feature learning. In the supervised branch, we re-evaluate the approach to multiscale feature aggregation and introduce an adaptive feature module to integrate features from both global and local perspectives. Furthermore, our method can serve as a general framework that is compatible with the existing CD approaches. Experimental results on four CD datasets demonstrate the superior effectiveness and generalization of our proposed method. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZijunTan/SWCD.</uri>