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

Fei Gao, Yishan He, Jun Wang, Fei Ma, Erfu Yang, Amir Hussain

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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.

Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
Subtitle of host publication10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Kaizhu Huang, Jiangbin Zheng, Jun Cai, Rongjun Chen, Yinyin Xiao
Place of PublicationCham, Switzerland
Number of pages11
ISBN (Print)9783030394301
Publication statusPublished - 1 Feb 2020
Event10th International Conference on Brain Inspired Cognitive Systems, BICS 2019 - Guangzhou, China
Duration: 13 Jul 201914 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11691 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Brain Inspired Cognitive Systems, BICS 2019


  • encoder-decoder network
  • fully-connected CRF
  • SAR image segmentation

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