A full-scale feature aggregation network for remote sensing image change detection

; Change detection (C D) is an important task of remote sensing, always facing many pseudo changes and large scale variations. However, existing methods mainly focus on modeling difference features and neglect extracting sufficient information from the original images, which affects feature discrimination and makes it difficult to distinguish change regions stably. To address these problems, a full-scale feature aggregation network (FFANet) is proposed to make fuller use of the original image features, which drives the generated feature representations to be semantically richer and spatially more precise, thus improving the network' s detection performance for small targets and target edges. Deep supervision is also extended to combine multi-scale prediction maps to drive the detection of different objects at more appropriate scales, thus improving the robustness of the network to object scale variations. On the CDD dataset, our proposed method improves the F1 -score by 0.034 compared to the baseline network by increasing the number of parameters by only 1.01Xl0 6 © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.