Ping Zhong
Ping Zhong
Verified email at nudt.edu.cn
TitleCited byYear
A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images
P Zhong, R Wang
IEEE Transactions on Geoscience and Remote Sensing 45 (12), 3978-3988, 2007
1492007
Learning conditional random fields for classification of hyperspectral images
P Zhong, R Wang
IEEE transactions on image processing 19 (7), 1890-1907, 2010
1212010
Learning to diversify deep belief networks for hyperspectral image classification
P Zhong, Z Gong, S Li, CB Schönlieb
IEEE Transactions on Geoscience and Remote Sensing 55 (6), 3516-3530, 2017
1162017
Explaining explanations: An approach to evaluating interpretability of machine learning
LH Gilpin, D Bau, BZ Yuan, A Bajwa, M Specter, L Kagal
arXiv preprint arXiv:1806.00069, 2018
81*2018
Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery
P Zhong, R Wang
IEEE Transactions on Geoscience and Remote Sensing 51 (4), 2260-2275, 2012
792012
Modeling and classifying hyperspectral imagery by CRFs with sparse higher order potentials
P Zhong, R Wang
IEEE Transactions on Geoscience and Remote Sensing 49 (2), 688-705, 2010
652010
Dynamic learning of SMLR for feature selection and classification of hyperspectral data
P Zhong, P Zhang, R Wang
IEEE Geoscience and Remote Sensing Letters 5 (2), 280-284, 2008
612008
Active learning with Gaussian process classifier for hyperspectral image classification
S Sun, P Zhong, H Xiao, R Wang
IEEE Transactions on Geoscience and Remote Sensing 53 (4), 1746-1760, 2014
472014
Learning sparse CRFs for feature selection and classification of hyperspectral imagery
P Zhong, R Wang
IEEE transactions on geoscience and remote sensing 46 (12), 4186-4197, 2008
442008
Using combination of statistical models and multilevel structural information for detecting urban areas from a single gray-level image
P Zhong, R Wang
IEEE transactions on geoscience and remote sensing 45 (5), 1469-1482, 2007
372007
Jointly learning the hybrid CRF and MLR model for simultaneous denoising and classification of hyperspectral imagery
P Zhong, R Wang
IEEE Transactions on Neural Networks and Learning Systems 25 (7), 1319-1334, 2014
342014
Diversity-promoting deep structural metric learning for remote sensing scene classification
Z Gong, P Zhong, Y Yu, W Hu
IEEE Transactions on Geoscience and Remote Sensing 56 (1), 371-390, 2017
272017
A MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery
S Sun, Z Ping, H Xiao, R Wang
IEEE Journal of Selected Topics in Signal Processing 9 (6), 1074 - 1088, 2015
232015
A CNN with multiscale convolution and diversified metric for hyperspectral image classification
Z Gong, P Zhong, Y Yu, W Hu, S Li
IEEE Transactions on Geoscience and Remote Sensing 57 (6), 3599-3618, 2019
192019
An unsupervised convolutional feature fusion network for deep representation of remote sensing images
Y Yu, Z Gong, C Wang, P Zhong
IEEE Geoscience and Remote Sensing Letters 15 (1), 23-27, 2017
182017
Unsupervised representation learning with deep convolutional neural network for remote sensing images
Y Yu, Z Gong, P Zhong, J Shan
International Conference on Image and Graphics, 97-108, 2017
182017
Object detection based on combination of conditional random field and markov random field
P Zhong, R Wang
18th International Conference on Pattern Recognition (ICPR'06) 3, 160-163, 2006
162006
-Regularized Deconvolution Network for the Representation and Restoration of Optical Remote Sensing Images
J Zhang, P Zhong, Y Chen, S Li
IEEE transactions on geoscience and remote sensing 52 (5), 2617-2627, 2013
142013
Image segmentation based on Markov random fields with adaptive neighborhood systems
P Zhong, R Wang
Optical Engineering 45 (9), 097202, 2006
122006
基于条件随机场的 DDoS 攻击检测方法
刘运, 蔡志平, 钟平, 殷建平, 程杰仁
软件学报, 2011
112011
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