A Siamese Network with a Multiscale Window-Based Transformer via an Adaptive Fusion Strategy for High-Resolution Remote Sensing Image Change Detection

Abstract

Remote sensing image change detection (RS-CD) has made impressive progress with the help of deep learning techniques. Small object change detection (SoCD) still faces many challenges. On the one hand, when the scale of changing objects varies greatly, deep learning models with overall accuracy as the optimization goal tend to focus on large object changes and ignore small object changes to some extent. On the other hand, the RS-CD model based on deep convolutional networks needs to perform multiple spatial pooling operations on the feature map to obtain deep semantic features, which leads to the loss of small object feature-level information in the local space. Therefore, we propose a Siamese transformer change detection network with a multiscale window via an adaptive fusion strategy (SWaF-Trans). To solve the problem of ignoring small object changes, we compute self-attention in windows of different scales to model changing objects at the corresponding scales and establish semantic information links through a moving window mechanism to capture more comprehensive small object features in small-scale windows, thereby enhancing the feature representation of multiscale objects. To fuse multiscale features and alleviate the problem of small object feature information loss, we propose a channel-related fusion mechanism to model the global correlation between channels for display and adaptively adjust the fusion weights of channels to enable the network to capture more discriminative features of interest and reduce small object feature information loss. Experiments on the CDD and WHU-CD datasets show that SWaF-Trans exceeds eight advanced baseline methods, with absolute F1 scores as high as 97.10% and 93.90%, achieving maximum increases of 2% and 5.6%, respectively, compared to the baseline methods.