MTASCD: A Semantic Change Detection Model Using Remote Sensing Images

Change detection (CD) technique has wide applications in remote sensing-based application. Most existing works regard CD as a task of pixel-level binary classification to distinguish changed areas from unchanged ones. In order to segment the changed areas and classify ground objects before and after the change, an attention mechanism augmented semantic change detection (SCD) model, Multi-Task learning and Attention mechanism based Semantic Change Detection (MTASCD), is developed, which translates SCD into the combination of binary-classification CD task and multi-classification object segmentation (MCOS) task, and fuses the semantic features from CD and MCOS for reliable segmentation and reduction of training complexity of model. In the proposed model, a Siamese network with shared weights is designed to extract the semantic features of ground objects and separate the false changes caused by environment like season change or lighting condition. The prediction result of the CD task feeds the MCOS task, and then fuses with the bi-temporal image features at the higher feature level so as to retain more detailed context information. Attention mechanism is introduced to promote the generalization and robustness of the model. Sufficient experiments are carried out on the open Land-CD dataset, and comparative results show that MTASCD can improve the accuracy of semantic change detection, especially for small-sized objects.