부산대학교 도서관

Change detection in satellite images is an important research area as it has a wide range of applications in natural resource monitoring, geo-hazard detections, urban planning, etc. Identifying physical changes on the ground and avoiding spurious changes due to other reasons like co-registration issues, change in illumination conditions, sun angle, and presence of cloud and fog is a challenging task. This work proposes a multitask learning based change detection model where two parallel pipeline architectures predict change map and image difference. The proposed model takes two images and their difference as input and provides them to a backbone network (BN). The output of the BN is fed into the proposed multiscale attention module for the effective identification of changes in multitemporal and very high-resolution aerial images. In another parallel path, the output of the BN is downsampled and passed to the proposed deconvolution with a subpixel convolution module to generate image difference. Two loss functions are utilized in two parallel paths to train the overall model in an end-to-end supervised setting. A comprehensive set of experiments have been carried out, and the results reveal that the proposed DRMNet model has achieved an F1 score improvement of 1.66% in CDD, 1.61% in SYSU, and 0.14% in LEVIR-CD datasets. It achieved an F1 score of 86.11% for the BCDD dataset with the new test image.