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Xiangyong Cao
Researcher at Xi'an Jiaotong University
Publications - 46
Citations - 1538
Xiangyong Cao is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Markov random field. The author has an hindex of 12, co-authored 26 publications receiving 712 citations. Previous affiliations of Xiangyong Cao include University of Derby.
Papers
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Journal ArticleDOI
Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network
TL;DR: A new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework and achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.
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Hyperspectral Image Classification With Convolutional Neural Network and Active Learning
TL;DR: This article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework and achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples.
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Denoising Hyperspectral Image With Non-i.i.d. Noise Structure
TL;DR: This paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes.
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Underwater image enhancement with global–local networks and compressed-histogram equalization
Xueyang Fu,Xiangyong Cao +1 more
TL;DR: This work proposes a two-branch network to compensate the global distorted color and local reduced contrast, respectively, and designs a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training.
Journal ArticleDOI
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
TL;DR: Wang et al. as mentioned in this paper proposed a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. But, their method requires a large number of patches to be used.