Dual-Attention-Guided Multiscale Feature Aggregation Network for Remote Sensing Image Change Detection

Abstract

Remote sensing image change detection plays an important role in urban planning and environmental monitoring. However, the existing change detection algorithms have limited ability in feature extraction, feature relationship understanding, and capture of small target features and edge detail features, which leads to the loss of some edge detail information and small target features. To this end, a new dual attention-guided multiscale feature aggregation network is proposed. In encoding stage, the fully convolutional dual-branch structure is used to extract the semantic features of different scales, and then the multi-scale adjacent semantic information aggregation module is used to aggregate the adjacent semantic features at different scales, which can better capture and fuse the features of different scales, thereby improving the accuracy and robustness of change detection. In decoding stage, the dual attention fusion module is proposed to guide and fuse the features extracted from different scales along the spatial and channel directions, and reduce the background noise interference. In addition, this paper also proposes a three-branch feature fusion module and a global semantic information enhancement module to make the network better integrate global semantics and differential semantics and further integrate high-level semantic features. We also introduce an auxiliary classifier in decoding stage to provide additional supervision signals, and fuse the output of the three auxiliary classifiers with the output of the main decoder to further achieve multi-scale feature fusion. The comparative experiments on three remote sensing data sets show that the proposed method is superior to the existing change detection methods.