SPATIAL-PRESERVING AND EDGE-ORIENTING HIGH-RESOLUTION NETWORK FOR REMOTE SENSING CHANGE DETECTION

Remote sensing change detection (RSCD), with a view to probing surface changes between bi-temporal images, makes a spurt of progress with the continuous innovation of deep learning. However, the extraction of multi-scale features and the detection of small domain of variation as well as the detail information in RSCD task still has large development space. Besides, current existing methods mostly focus on learning regional information but pay less regard to boundary identification, which leads to inaccurate detection results. Therefore, a spatial-preserving and edge-orienting high-resolution network is proposed to address the problems. In the overall architecture, a dual-branch encoder consists of a pyramid feature extracted branch and an enhanced HR network branch is designed to extract muti-scale bi-temporal features and small change objectives, while two edge-orienting modules (EOM) are embedded in order to utilize edge prior knowledge for further improving the accuracy of change detection. Moreover, spatial-preserving module (SPM) based on the self-attention calculation in spatial dimension is applied in the pyramid part to alleviate the poor location information of the high-level features. The experimental results demonstrate that the proposed network outperforms the cited state-of-the-art methods on LEVIR change detection datasets (LEVIR-CD).