MULTI-TASK LEARNING FOR SEMANTIC CHANGE DETECTION ON VHR REMOTE SENSING IMAGES

Remote Sensing Images Change Detection (RSICD) aims to locate the changed regions between bitemporal very-high-resolution (VHR) sensing images. However, existing deep learning-based RSICD methods are from the requirements by practical application, mainly due to the low feature discrimination and limited accuracy. We propose a novel multi-task and multi-temporal encoder-decoder changed detection network (MMNet) for VHR images, which accomplished both semantic segmentation and change detection at the same time. The encoder extracts multi-level contextual information, which contains two semantic segmentation branches (SSB) and a change detection branch (CDB). In this way, change representation constrains semantic representation during training, which introduces a novel loss function to ensure the semantic consistency within the unchanged regions. Furthermore, to utilize multi-level feature representation for enhancing the separability of features, a multi-scale feature fusion module (MFFM) is presented.