A high-resolution remote sensing image change detection network based on U-Net++ and attention mechanism

Remote sensing image change detection is an important task in the field of remote sensing image analysis, and it is widely used in urban planning, disaster detection, environmental protection and other fields. A U-Net++ based remote sensing image change detection network is proposed to address the issues of complex backgrounds, diverse types of changes, missed detections, and rough boundary recognition in high-resolution remote sensing images in change detection tasks. This algorithm uses U-Net++ as the backbone extraction network, and applies a Siamese neural network structure in its encoder to extract features from two different time images. In the convolutional part, the CBAM attention module and Mish activation function are fused to improve the network's feature extraction ability. In addition, the MSOF strategy is used to fuse the results of different levels of the U-Net++ network to output the final result map to improve the accuracy of the network.