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Open AccessArticle

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

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School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
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Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
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Cognitive Big Data and Cyber-Informatics (CogBID) Laboratory, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, Scotland, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 512; https://doi.org/10.3390/rs11050512
Received: 24 January 2019 / Revised: 21 February 2019 / Accepted: 26 February 2019 / Published: 2 March 2019
(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)
Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen’s kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks. View Full-Text
Keywords: synthetic aperture radar (SAR); segmentation; conditional random fields (CRF); conditional generative adversarial nets (CGAN); neighborhood consistency synthetic aperture radar (SAR); segmentation; conditional random fields (CRF); conditional generative adversarial nets (CGAN); neighborhood consistency
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Ma, F.; Gao, F.; Sun, J.; Zhou, H.; Hussain, A. Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF. Remote Sens. 2019, 11, 512.

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