Pixel-wise segmentation of SAR imagery using encoder-decoder network and fully-connected CRF

Gao, Fei and He, Yishan and Wang, Jun and Ma, Fei and Yang, Erfu and Hussain, Amir; Ren, Jinchang and Hussain, Amir and Zhao, Huimin and Huang, Kaizhu and Zheng, Jiangbin and Cai, Jun and Chen, Rongjun and Xiao, Yinyin, eds. (2020) Pixel-wise segmentation of SAR imagery using encoder-decoder network and fully-connected CRF. In: Advances in Brain Inspired Cognitive Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer, CHN, pp. 155-165. ISBN 9783030394301

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    Abstract

    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.