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Open AccessArticle

An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial 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
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(2), 216; https://doi.org/10.3390/rs12020216
Received: 6 December 2019 / Revised: 3 January 2020 / Accepted: 3 January 2020 / Published: 8 January 2020
Due to the development of deep convolutional neural networks (CNNs), great progress has been made in semantic segmentation recently. In this paper, we present an end-to-end Bayesian segmentation network based on generative adversarial networks (GANs) for remote sensing images. First, fully convolutional networks (FCNs) and GANs are utilized to realize the derivation of the prior probability and the likelihood to the posterior probability in Bayesian theory. Second, the cross-entropy loss in the FCN serves as an a priori to guide the training of the GAN, so as to avoid the problem of mode collapse during the training process. Third, the generator of the GAN is used as a teachable spatial filter to construct the spatial relationship between each label. Some experiments were performed on two remote sensing datasets, and the results demonstrate that the training of the proposed method is more stable than other GAN based models. The average accuracy and mean intersection (MIoU) of the two datasets were 0.0465 and 0.0821, and 0.0772 and 0.1708 higher than FCN, respectively. View Full-Text
Keywords: image semantic segmentation; Bayesian; generative adversarial networks (GAN); fully convolutional networks (FCN); synthetic aperture radar (SAR) image semantic segmentation; Bayesian; generative adversarial networks (GAN); fully convolutional networks (FCN); synthetic aperture radar (SAR)
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Xiong, D.; He, C.; Liu, X.; Liao, M. An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial Network for Remote Sensing Images. Remote Sens. 2020, 12, 216.

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