LAUSR

We present a convolutional neural network (CNN)-based approach for scene-level change detection in aerial images with registration errors. Thousands of aerial images and long videos are routinely acquired for monitoring large areas, such as forests and oil pipelines. Annotating changes in those videos and images can be tedious, error-prone, or even unfeasible for a human operator. Moreover, accurate pixel-wise registration is usually unavailable, and conventional descriptor-based registration methods are doomed to fail, since they rely on similarities to establish correspondences that are impaired due to the latent changes in the scene. We introduce a new neural network architecture that can be trained end-to-end to simultaneously perform image registration and change detection to mitigate these issues. Our approach reduces the number of parameters required to optimize the process while steeping toward a more robust change detection pipeline for unmanned aerial vehicle (UAV) images. We evaluated our method in two datasets containing registration errors of up to 96 pixels in translation and 30° in rotation. The results showed that our approach outperformed the state of the art, achieving a 6% improvement in the area under the curve (AUC) metric.