Pixel-Wise Segmentation of SAR Imagery Using Encoder-Decoder Network and Fully-Connected CRF

Synthetic Aperture Radar (SAR) image segmentation is an important step in SAR image interpretation. Common Patch-based methods treat all the pixels within the patch as a single category and do not take the label consistency between neighbor patches into consideration, which makes the segmentation results less accurate. In this paper, we use an encoder-decoder network to conduct pixel-wise segmentation. Then, in order to make full use of the contextual information between patches, we use fully-connected conditional random field to optimize the combined probability map output from encoder-decoder network. The testing results on our SAR data set shows that our method can effectively maintain contextual information of pixels and achieve better segmentation results.