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 | 321 | 2017 |
Multiscale dynamic graph convolutional network for hyperspectral image classification S Wan, C Gong, P Zhong, B Du, L Zhang, J Yang IEEE Transactions on Geoscience and Remote Sensing 58 (5), 3162-3177, 2019 | 283 | 2019 |
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 | 219 | 2007 |
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 | 190 | 2019 |
Learning conditional random fields for classification of hyperspectral images P Zhong, R Wang IEEE transactions on image processing 19 (7), 1890-1907, 2010 | 157 | 2010 |
Diversity in machine learning Z Gong, P Zhong, W Hu Ieee Access 7, 64323-64350, 2019 | 155 | 2019 |
Unsupervised representation learning with deep convolutional neural network for remote sensing images Y Yu, Z Gong, P Zhong, J Shan Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China …, 2017 | 144 | 2017 |
Hyperspectral image classification with context-aware dynamic graph convolutional network S Wan, C Gong, P Zhong, S Pan, G Li, J Yang IEEE Transactions on Geoscience and Remote Sensing 59 (1), 597-612, 2020 | 122 | 2020 |
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 | 116 | 2012 |
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 | 92 | 2014 |
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 | 90 | 2017 |
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 | 78 | 2010 |
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 | 75 | 2008 |
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 | 72 | 2014 |
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 | 58 | 2017 |
Statistical loss and analysis for deep learning in hyperspectral image classification Z Gong, P Zhong, W Hu IEEE transactions on neural networks and learning systems 32 (1), 322-333, 2020 | 52 | 2020 |
Automatic graph learning convolutional networks for hyperspectral image classification J Chen, L Jiao, X Liu, L Li, F Liu, S Yang IEEE Transactions on Geoscience and Remote Sensing 60, 1-16, 2021 | 50 | 2021 |
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 | 46 | 2015 |
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 | 45 | 2008 |
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 | 42 | 2007 |