Background-Driven and Foreground-Refined Network for Weakly Supervised Change Detection

Change detection (CD) in remote sensing aims to reveal meaningful surface changes and has been flourishing in recent years. Compared with fully-supervised methods based on pixel-level labels, image-level labels are easy to acquire, which reduces manual labor to a large extent. However, image-level labels lack spatial-and-shape information while containing the least semantic information, which poses a great challenge to the weakly-supervised CD task. Motivated by the prior that bi-temporal images have background semantic consistency, we propose Background-Driven and Foreground-Refined (BDFR-Net) to ameliorate the above problem. Specifically, there are two key components in the proposed method: the Background-Driven Reconstruction (BDR) with image-level supervision and the Foreground-Refined Learning (FRL) with affinity learning. The former generates changed regions of foreground and background separation, which activates the foreground from image-level supervision and constrains the foreground by maintaining spatial and semantic consistency in background regions. The latter introduces Complementary Fusion and Label Adaption (CFLA) strategies to further refine the foreground, which can mine complementary information from foreground sequences and suppress false activations. In addition, affinity learning is proposed to stabilize and supervise the above process. Complementary relationships between foreground and background are fully utilized. Tested on two popular CD datasets, the results demonstrate that our proposed BDFR-Net produces completely changed regions with clear boundaries and outperforms state-of-the-art weakly-supervised methods.