Modified Dual Domain Network for SAR Change Detection

Synthetic Aperture Radar (SAR) images are utilised for change detection analysis due to their all-weather imaging capabilities. This paper proposes modified dual domain network (MDDNet) for SAR change detection. We introduced the atrous spatial pyramid pooling block to extract multiscale characteristics in the spatial domain. The MDDNet extracts features from both the spatial and frequency domains. The proposed network is trained unsupervised with pre-classification output. The performances of proposed and existing SAR change detection models are evaluated on four bitemporal SAR datasets. The experimental results indicate that the results of proposed MDDNet is better than existing change detection models on four bitemporal SAR dataset.