Prati
Weiwei Sun (孙伟伟)
Weiwei Sun (孙伟伟)
Potvrđena adresa e-pošte na nbu.edu.cn
Naslov
Citirano
Citirano
Godina
Hyperspectral band selection: A review
W Sun, Q Du
IEEE Geoscience and Remote Sensing Magazine 7 (2), 118-139, 2019
2022019
Band selection using improved sparse subspace clustering for hyperspectral imagery classification
W Sun, L Zhang, B Du, W Li, YM Lai
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2015
1492015
Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery
W Sun, C Liu, J Li, YM Lai, W Li
Journal of Applied Remote Sensing 8 (1), 083641-083641, 2014
1282014
R-CNN-based ship detection from high resolution remote sensing imagery
S Zhang, R Wu, K Xu, J Wang, W Sun
Remote Sensing 11 (6), 631, 2019
1192019
Graph-regularized fast and robust principal component analysis for hyperspectral band selection
W Sun, Q Du
IEEE Transactions on Geoscience and Remote Sensing 56 (6), 3185-3195, 2018
1182018
Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland
Q Jiang, W Li, Z Fan, X He, W Sun, S Chen, J Wen, J Gao, J Wang
Journal of hydrology 595, 125660, 2021
1132021
A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification
W Sun, G Yang, B Du, L Zhang, L Zhang
IEEE Transactions on Geoscience and Remote Sensing 55 (7), 4032-4046, 2017
872017
Self-paced joint sparse representation for the classification of hyperspectral images
J Peng, W Sun, Q Du
IEEE Transactions on Geoscience and Remote Sensing 57 (2), 1183-1194, 2018
842018
UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification
W Sun, A Halevy, JJ Benedetto, W Czaja, C Liu, H Wu, B Shi, W Li
ISPRS Journal of Photogrammetry and Remote Sensing 89, 25-36, 2014
792014
Susceptibility evaluation and mapping of China’s landslides based on multi-source data
C Liu, W Li, H Wu, P Lu, K Sang, W Sun, W Chen, Y Hong, R Li
Natural Hazards 69, 1477-1495, 2013
792013
A dissimilarity-weighted sparse self-representation method for band selection in hyperspectral imagery classification
W Sun, L Zhang, L Zhang, YM Lai
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016
692016
The advanced hyperspectral imager: aboard China's gaoFen-5 satellite
YN Liu, DX Sun, XN Hu, X Ye, YD Li, SF Liu, KQ Cao, MY Chai, J Zhang, ...
IEEE Geoscience and Remote Sensing Magazine 7 (4), 23-32, 2019
672019
Spatial–spectral squeeze-and-excitation residual network for hyperspectral image classification
L Wang, J Peng, W Sun
Remote Sensing 11 (7), 884, 2019
662019
Fast and latent low-rank subspace clustering for hyperspectral band selection
W Sun, J Peng, G Yang, Q Du
IEEE Transactions on Geoscience and Remote Sensing 58 (6), 3906-3915, 2020
622020
Discriminative transfer joint matching for domain adaptation in hyperspectral image classification
J Peng, W Sun, L Ma, Q Du
IEEE Geoscience and Remote Sensing Letters 16 (6), 972-976, 2019
592019
A large-scale benchmark data set for evaluating pansharpening performance: Overview and implementation
X Meng, Y Xiong, F Shao, H Shen, W Sun, G Yang, Q Yuan, R Fu, ...
IEEE Geoscience and Remote Sensing Magazine 9 (1), 18-52, 2020
572020
Low-rank and sparse representation for hyperspectral image processing: A review
J Peng, W Sun, HC Li, W Li, X Meng, C Ge, Q Du
IEEE Geoscience and Remote Sensing Magazine 10 (1), 10-43, 2021
532021
Fast and robust self-representation method for hyperspectral band selection
W Sun, L Tian, Y Xu, D Zhang, Q Du
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2017
532017
Nonlinear dimensionality reduction via the ENH-LTSA method for hyperspectral image classification
W Sun, A Halevy, JJ Benedetto, W Czaja, W Li, C Liu, B Shi, R Wang
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE …, 2014
502014
A hierarchical classification framework of satellite multispectral/hyperspectral images for mapping coastal wetlands
L Jiao, W Sun, G Yang, G Ren, Y Liu
Remote Sensing 11 (19), 2238, 2019
442019
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