Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves

Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs’ low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks.