SAR Image Change Detection Method Based on Neural-CRF Structure

There are two problems in SAR image change detection when using difference images (DIs), i.e., 1) the subtraction operation results in serious loss of semantic information in DIs; and 2) the boundary of DI is uncertain. We propose a change detection method based on Neural-CRF structure. Firstly, Transformer-UNet (TR-UNet) is designed to provide the unary potential for CRF. The TR-Attention module improves the semantic expression ability of UNet by introducing the multi-head attention mechanism of TR. Secondly, a cascade CRF as Recurrent Neural Network, named as C-CRF-RNN, is proposed to update the unary potential and pairwise potential simultaneously. This improves the ability of CRF-RNN to refine pixel-level label prediction. Experiments show that the proposed method consistently outperforms the state-of-the-art methods on two benchmarks including berne data and ottawa data.