Remote Sensing Image Change Detection based on Improved DeepLabv3+ Siamese Network

Traditional remote sensing image change detection method relies on artificial construction of features, and the algorithm design is complex and has a low accuracy. Moreover, the remote sensing image change detection technique, which superimposes two different phase images and then inputs them into the neural network, will cause the interaction of characteristics of different phases. It is difficult to maintain the high-dimensional features of the original image, and the model is less robust. Therefore, this paper proposes a remote sensing image change detection method that improves the DeepLabv3+ Siamese network based on the encoding and decoding structure of the classic DeepLabv3+ network: 1) In the encoding stage, the features are extracted by the Siamese network sharing weights, and remote sensing images are received through two input terminals respectively, so as to preserve the high-dimensional features of different time-phase images; 2) The dense atrous space pyramid pooling model replaces the atrous space pyramid pooling model in feature fusion. In addition, the method that combines the output of each atrous convolution through dense connections improves the segmentation of objects of different scales; 3) In the decoding stage, multiple levels of feature map information contain variance that causes integration problems. As a result, a feature alignment model based on the attention mechanism is introduced to guide the feature alignment of different levels, and then strengthen the learning of critical features to enhance model robustness. The open-source dataset CDD is used to verify the efficacy of the method in this paper, compared with UNet-EF, FC-Siam-conc, Siam-DeepLabv3+ and N-Siam-DeepLabv3+ networks. The test results demonstrate that the presented approach in the study achieves 87.3%, 90.2%, 88.4%, 96.4% in precision rate, recall rate, F1 value, and overall accuracy, respectively, which are higher than those of the UNet-EF, FC-Siam-conc, Siam-DeepLabv3+ and N-Siam-DeepLabv3+ networks. The detection results turn out to be more comprehensive, and the boundary detection is smoother and more robust to scale changes. © 2022, Science Press. All right reserved.