2020
DOI: 10.3390/rs12020216
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An End-To-End Bayesian Segmentation Network Based on a Generative Adversarial Network for Remote Sensing Images

Abstract: 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 a… Show more

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Cited by 18 publications
(3 citation statements)
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“…Nearly all applications of Bayesian deep learning (BDL) methods in the literature arise in medical imaging or autonomous driving domains [5,6]. Only one paper has explored BDL in the SAR domain [31]. However, this paper only achieves a maximum a posteriori (MAP) point estimate of uncertainty, as their generative adversarial network (GAN)-based approach is deterministic: it takes a previous network's outputs as its inputs, as opposed to the Gaussian noise that a standard GAN receives.…”
Section: Prior Workmentioning
confidence: 99%
“…Nearly all applications of Bayesian deep learning (BDL) methods in the literature arise in medical imaging or autonomous driving domains [5,6]. Only one paper has explored BDL in the SAR domain [31]. However, this paper only achieves a maximum a posteriori (MAP) point estimate of uncertainty, as their generative adversarial network (GAN)-based approach is deterministic: it takes a previous network's outputs as its inputs, as opposed to the Gaussian noise that a standard GAN receives.…”
Section: Prior Workmentioning
confidence: 99%
“…In recent years, a range of dehazing methods have been developed, roughly divided into prior and deep learning-based methods [7,8]. Most prior-based methods are based on the atmospheric scattering model and require accurate estimation for the transmission and atmospheric light [9].…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning is useful for automating various tasks in radiation oncology, most notably organ segmentation but has also been used for dose calculations and linear accelerator quality assurance [14][15][16][17][18][19][20][21]. Generative deep learning models such as generative adversarial networks have been used in other domains to perform image-to-image translation tasks such as: converting photographs taken during the day to night, converting hand-drawn pictures into realistic photos, and converting satellite images into maps [22][23][24][25][26][27]. They have also been used in medical applications to perform tasks such as converting MRI datasets into synthetic CT datasets [28][29][30][31][32][33][34].…”
Section: Introductionmentioning
confidence: 99%