OFACD: An end-to-end change detection network for small UAVs remote sensing with viewpoint differences

Abstract

Change detection using remote sensing images captured by small unmanned aerial vehicles (small UAVs) finds wide applications across various fields. However, there is a challenge when dealing with images captured at the same location by small UAVs at different times, leading to differences in viewpoint. These viewpoint differences present a significant challenge for most change detection methods. In this paper, we propose an end-to-end network, OFACD, designed to simultaneously address the issues of image alignment and change detection. Our network aligns feature maps using estimated optical flow and performs change detection concurrently. This approach enables the network to directly process images with viewpoint differences, effectively improving performance in scenarios with accumulated errors or large viewpoint variations, as well as enhancing throughput by eliminating repetitive feature extraction. Additionally, to fill the gap of the absence of change detection datasets with viewpoint differences and to evaluate our model, we created two change detection datasets with viewpoint differences. Extensive experimental results demonstrate that our method outperforms several state-of-the-art change detection methods in datasets involving viewpoint differences, exhibiting superior throughput and performance.