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An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images

1
Electronic and Information School, Wuhan University, Wuhan 430072, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1604; https://doi.org/10.3390/rs11131604
Received: 15 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 5 July 2019
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively. View Full-Text
Keywords: generative adversarial network; conditional random fields; semantic segmentation; loss function generative adversarial network; conditional random fields; semantic segmentation; loss function
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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He, C.; Fang, P.; Zhang, Z.; Xiong, D.; Liao, M. An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images. Remote Sens. 2019, 11, 1604.

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