Abstract: To address the problems of reduced feature map resolution in deep convolutional neural networks, which leads to poor performance in detecting small changes in remote sensing images and difficulty in effectively distinguishing external interference to produce false changes, a Siamese attention-gated fusion encoding-decoding network for remote sensing image change detection is proposed. A triple attention network module is introduced in the encoding part to further solve the problem of false changes in the change detection image. An attention-gated fusion module is proposed to selectively fuse features from multiple levels. A deep supervision strategy is directly introduced in the decoding part to enhance the feature extraction capability of the change detection network. The effectiveness of the proposed network is verified through experiments.

Key words: image processing, high-resolution remote sensing image, change detection, attention mechanism, gated fusion, deep supervision