UAL: Unchanged Area Loss-Function for Change Detection Networks

A novel change detection approach is proposed in this paper, which is based on dual task learning model and unchanged area loss-function(UAL). Dual task model combines change detection(CD) and semantic segmentation(SS) based on Siamese neural network to improve the feature separability, and UAL aims to establish the semantic label correspondence within unchanged regions. Experiments demonstrate the effectiveness and advantages of the proposed approach. Our code and models are available at https://github.com/Chuan-shanjial/A-loss-function-for-change-detection.