Synthetic aperture radar image change detection using saliency detection and attention capsule network

Abstract

Synthetic aperture radar (SAR) image change detection has been widely applied in a variety of fields as one of the research hotspots in remote sensing image processing. To increase the accuracy of SAR image change detection, an algorithm based on saliency detection and an attention capsule network is proposed. First, the difference image (DI) is processed using the saliency detection method. The DI’s most significant regions are extracted. Considering the saliency detection characteristics, we select training samples only from the DI’s most salient regions. The regions in the background are omitted. This results in a significant reduction in the number of training samples. Second, a capsule network based on an attention mechanism is constructed. The spatial attention model is capable of extracting the salient characteristics. Capsule networks enable precise classification. Finally, a final change map is obtained using capsule network to classify images. To compare the proposed method with the related methods, experiments are carried out on four real SAR datasets. The results show that the proposed method is effective in improving the exactitude of change detection.