Adaptive Fusion NestedUNet for Change Detection Using Optical Remote Sensing Images

Change detection (CD) is a major topic in remote sensing research. Deep learning (DL)-based CD methods have made great progress. However, existing CD methods have difficulty in exploiting the different semantic and detailed information in deep and shallow features, which often leads to blurred target boundaries of identified changes. In addition, most CD methods based on the NestedUNet structure focus on improving accuracy, ignoring the importance of efficiency. Therefore, in this article, an adaptive fusion NestedUNet for CD (AFNUNet) is proposed. AFNUNet compresses the model parameters and computational cost by using the encoder based on the inverted bottleneck structure and the decoder based on the depthwise convolution. The correlation between the final multilevel feature maps extracted by NestedUNet is difficult to model by summation or concatenation. Therefore, an attention mechanism-based adaptive fusion module (AFM) is proposed. The AFM allows the network to adaptively select feature information from the final different layers of features extracted from NestedUNet in both channel and spatial dimensions so that the fused features capture deep rich semantic information while retaining detailed information at shallow boundaries. Finally, a loss function based on the Bray-Curtis distance is introduced for suppressing the sample imbalance problem. Extensive experiments on the WHU-CD, LEVIR-CD, and SYSU-CD datasets demonstrate that AFNUNet surpasses several state-of-the-art (SOTA) CD methods in terms of effectiveness. Moreover, the proposed AFNUNet remarkably reduces Params and FLOPs by 63% and 70% compared to other NestedUNet-based CD models.