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/Chuanshanjia/A-loss-function-for-change-detection.