A Remote Sensing Image Change Detection Network With Feature Constraints From a Visual Foundation Model

Zhaoming Wu, Luyang Zan, Zhengchao Chen, Mingyong Cai, Yixiang Li, Zeqing Wang, Jun Xie, Xuewei Shi

Abstract: Change detection aims to identify surface changes by analyzing remote sensing imagery acquired at different time points. A wide range of deep learning methods have been developed, typically relying on carefully designed models trained on limited publicly available datasets. Moreover, some studies have explored the application of visual foundation models with a large amount of prior knowledge to change detection tasks. However, the existing change detection networks based on vision foundation models ignore the semantic constraints between multitemporal images. To address this issue, this article presents an advanced vision foundation model-constrained change detection network, which effectively integrates features from vision foundation models into the change detection task. The proposed network is designed from the perspectives of feature transfer and feature decoding to improve the extraction performance of changing regions. First, we introduce the semantic feature transfer module, which uses a cross-attention mechanism to effectively transfer visual features to the change detection network. We subsequently develop a semantic feature-constrained decoder that directly incorporates visual features into the decoder to extract the semantic information of change regions. A feature-level loss constraint is then applied based on the ground truth and the predicted change results. To verify the effectiveness and robustness of our proposed method, we conduct comparative experiments on multiple datasets. The results demonstrate that our approach achieves state-of-the-art performance. In addition, we construct an application-oriented change detection dataset (Shandong Change Detection Dataset) using selected remote sensing imagery from Shandong Province. The distribution map of the change detection results in Shandong Province further proves the feasibility of our proposed model in large-scale applications.

External IDs:dblp:journals/staeors/WuZCCLWXS25