Chang, C.I.[Chein-I],
Wang, S.,
Constrained Band Selection for Hyperspectral Imagery,
GeoRS(44), No. 6, June 2006, pp. 1575-1585.
IEEE DOI
0606
BibRef
Chang, C.I.[Chein-I],
Liu, K.H.[Keng-Hao],
Progressive Band Selection of Spectral Unmixing for Hyperspectral
Imagery,
GeoRS(52), No. 4, April 2014, pp. 2002-2017.
IEEE DOI
1403
geophysical signal processing
See also Band Subset Selection for Anomaly Detection in Hyperspectral Imagery.
BibRef
Liu, K.H.[Keng-Hao],
Chen, S.Y.[Shih-Yu],
Chien, H.C.[Hung-Chang],
Lu, M.H.[Meng-Han],
Progressive Sample Processing of Band Selection for Hyperspectral
Image Transmission,
RS(10), No. 3, 2018, pp. xx-yy.
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BibRef
Martínez Sotoca, J.[José],
Pla, F.,
Salvador Sánchez, J.,
Band Selection in Multispectral Images by Minimization of Dependent
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SMC-C(37), No. 2, March 2007, pp. 258-267.
IEEE DOI
0703
BibRef
Martínez Sotoca, J.[José],
Pla, F.[Filiberto],
Hyperspectral Data Selection from Mutual Information Between Image
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SSPR06(853-861).
Springer DOI
0608
BibRef
Martínez Sotoca, J.[José],
Salvador Sánchez, J.,
Pla, F.,
Attribute relevance in multiclass data sets using the naive bayes rule,
ICPR04(III: 426-429).
IEEE DOI
0409
BibRef
Martínez Sotoca, J.[José],
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Unsupervised band selection for multispectral images using information
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ICPR04(III: 510-513).
IEEE DOI
0409
BibRef
Ball, J.E.,
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Level Set Hyperspectral Image Classification Using Best Band Analysis,
GeoRS(45), No. 10, October 2007, pp. 3022-3027.
IEEE DOI
0711
BibRef
Martínez-Usó, A.[Adolfo],
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Martínez Sotoca, J.[José],
García-Sevilla, P.[Pedro],
Clustering-Based Hyperspectral Band Selection Using Information
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GeoRS(45), No. 12, December 2007, pp. 4158-4171.
IEEE DOI
0711
BibRef
Earlier: A1, A2, A4, A3:
Automatic Band Selection in Multispectral Images Using Mutual
Information-Based Clustering,
CIARP06(644-654).
Springer DOI
0611
BibRef
And: A1, A2, A3, A4:
Clustering-based multispectral band selection using mutual information,
ICPR06(II: 760-763).
IEEE DOI
0609
BibRef
Martinez Sotoca, J.[Jose],
Pla, F.[Filiberto],
Supervised feature selection by clustering using conditional mutual
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Elsevier DOI
1003
Supervised feature selection; Clustering; Conditional mutual information
See also Comments on supervised feature selection by clustering using conditional mutual information-based distances.
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AppRS(1), 2007, pp. 013509.
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Chen, G.,
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Evaluation and comparison of dimensionality reduction techniques
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CanRS(34), No. 1, 2008, pp. 26-36.
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Chen, G.,
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Denoising and dimensionality reduction of hyperspectral imagery
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JRS(30), No. 18, 2009, pp. 4889-4895, 2009.
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Chen, G.,
Qian, S.E.,
Simultaneous dimensionality reduction and denoising of
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CanRS(34), No. 5, 2008, pp. 447-454, 2008.
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Qian, S.E.[Shen-En],
Dimensionality reduction of multidimensional satellite imagery,
SPIE(Newsroom), March 21, 2011.
DOI Link
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Novel techniques can reduce dimensionality to derive better
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BibRef
Qian, S.E.[Shen-En],
Enhancing space-based signal-to-noise ratios without
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SPIE(Newsroom), January 5, 2011.
DOI Link
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A newly developed signal-processing technology based on wavelets can
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Chen, G.,
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Denoising of Hyperspectral Imagery Using Principal Component Analysis
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IEEE DOI
1103
BibRef
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1212
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Vaiphasa, C.[Chaichoke],
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Vaiphasa, T.[Tanasak],
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PandRS(62), No. 3, August 2007, pp. 225-235.
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0709
Artificial_Intelligence; Classification; Hyper spectral; Mangrove;
Remote sensing; Vegetation
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0710
BibRef
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IEEE DOI
1102
BibRef
Feng, J.[Jie],
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Hyperspectral Band Selection Based on Trivariate Mutual Information
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GeoRS(52), No. 7, July 2014, pp. 4092-4105.
IEEE DOI
1403
Approximation methods
BibRef
Jia, S.[Sen],
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A Novel Ranking-Based Clustering Approach for Hyperspectral Band
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GeoRS(54), No. 1, January 2016, pp. 88-102.
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1601
geophysical image processing
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Dimensionality Reduction by Spatial-Spectral Preservation in Selected
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GeoRS(55), No. 9, September 2017, pp. 5185-5197.
IEEE DOI
1709
selected band, spatial-spectral preservation,
determinantal point process (DPP),
BibRef
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Zoej, M.J.V.[Mohammad Javad Valadan],
Mokhtarzade, M.[Mehdi],
Reducing the Effect of the Endmembers' Spectral Variability by
Selecting the Optimal Spectral Bands,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link
1711
BibRef
Feng, J.[Jie],
Jiao, L.C.[Li-Cheng],
Sun, T.[Tao],
Liu, H.,
Zhang, X.R.[Xiang-Rong],
Multiple Kernel Learning Based on Discriminative Kernel Clustering
for Hyperspectral Band Selection,
GeoRS(54), No. 11, November 2016, pp. 6516-6530.
IEEE DOI
1610
Complexity theory
BibRef
Feng, J.[Jie],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Sun, T.[Tao],
Zhang, X.R.[Xiang-Rong],
Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection
With High Discrimination, High Information, and Low Redundancy,
GeoRS(53), No. 5, May 2015, pp. 2956-2969.
IEEE DOI
1502
approximation theory
BibRef
Nakamura, R.Y.M.,
Garcia Fonseca, L.M.,
dos Santos, J.A.[Jefersson A.],
da Silva Torres, R.[Ricardo],
Yang, X.S.[Xin-She],
Papa, J.P.[J. Papa],
Nature-Inspired Framework for Hyperspectral Band Selection,
GeoRS(52), No. 4, April 2014, pp. 2126-2137.
IEEE DOI
1403
geophysical image processing
BibRef
Geng, X.,
Sun, K.,
Ji, L.,
Zhao, Y.,
A Fast Volume-Gradient-Based Band Selection Method for Hyperspectral
Image,
GeoRS(52), No. 11, November 2014, pp. 7111-7119.
IEEE DOI
1407
Computational complexity
BibRef
Yuan, Y.[Yuan],
Zhu, G.K.[Guo-Kang],
Wang, Q.[Qi],
Hyperspectral Band Selection by Multitask Sparsity Pursuit,
GeoRS(53), No. 2, February 2015, pp. 631-644.
IEEE DOI
1411
data visualisation
BibRef
Patra, S.,
Modi, P.,
Bruzzone, L.,
Hyperspectral Band Selection Based on Rough Set,
GeoRS(53), No. 10, October 2015, pp. 5495-5503.
IEEE DOI
1509
feature selection
BibRef
Barman, B.[Barnali],
Patra, S.[Swarnajyoti],
Empirical study of neighbourhood rough sets based band selection
techniques for classification of hyperspectral images,
IET-IPR(13), No. 8, 20 June 2019, pp. 1266-1279.
DOI Link
1906
BibRef
Gholizadeh, H.[Hamed],
Mojaradi, B.[Barat],
Zoej, M.J.V.[Mohammad Javad Valadan],
Local Prototype Space-based Band Selection for Hyperspectral Subpixel
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PFG(2015), No. 5, 2015, pp. 373-380.
DOI Link
1512
BibRef
Zhu, G.K.[Guo-Kang],
Huang, Y.C.[Yuan-Cheng],
Lei, J.S.[Jing-Sheng],
Bi, Z.Q.[Zhong-Qin],
Xu, F.F.[Fei-Fei],
Unsupervised Hyperspectral Band Selection by Dominant Set Extraction,
GeoRS(54), No. 1, January 2016, pp. 227-239.
IEEE DOI
1601
benchmark testing
BibRef
Gong, M.G.[Mao-Guo],
Zhang, M.Y.[Ming-Yang],
Yuan, Y.[Yuan],
Unsupervised Band Selection Based on Evolutionary Multiobjective
Optimization for Hyperspectral Images,
GeoRS(54), No. 1, January 2016, pp. 544-557.
IEEE DOI
1601
decision making
BibRef
Sun, W.W.[Wei-Wei],
Jiang, M.[Man],
Li, W.[Weiyue],
Liu, Y.N.[Yin-Nian],
A Symmetric Sparse Representation Based Band Selection Method for
Hyperspectral Imagery Classification,
RS(8), No. 3, 2016, pp. 238.
DOI Link
1604
BibRef
Li, S.J.[Shi-Jin],
Zheng, Z.[Zhan],
Wang, Y.M.[Ya-Ming],
Chang, C.[Chun],
Yu, Y.F.[Yu-Feng],
A new hyperspectral band selection and classification framework based
on combining multiple classifiers,
PRL(83, Part 2), No. 1, 2016, pp. 152-159.
Elsevier DOI
1609
Hyperspectral imaging
BibRef
Feng, S.,
Itoh, Y.,
Parente, M.,
Duarte, M.F.,
Hyperspectral Band Selection From Statistical Wavelet Models,
GeoRS(55), No. 4, April 2017, pp. 2111-2123.
IEEE DOI
1704
geophysical image processing
BibRef
Yang, R.L.[Rong-Lu],
Su, L.F.[Li-Fan],
Zhao, X.B.[Xi-Bin],
Wan, H.[Hai],
Sun, J.G.[Jia-Guang],
Representative band selection for hyperspectral image classification,
JVCIR(48), No. 1, 2017, pp. 396-403.
Elsevier DOI
1708
High, dimensional, image
BibRef
Yu, C.,
Lee, L.C.,
Chang, C.I.,
Xue, B.,
Song, M.,
Chen, J.,
Band-Specified Virtual Dimensionality for Band Selection:
An Orthogonal Subspace Projection Approach,
GeoRS(56), No. 5, May 2018, pp. 2822-2832.
IEEE DOI
1805
Computer science, Detectors, Hyperspectral imaging,
Signal detection, Signal to noise ratio, Band selection (BS),
virtual dimensionality (VD)
BibRef
Sun, W.,
Du, Q.,
Graph-Regularized Fast and Robust Principal Component Analysis for
Hyperspectral Band Selection,
GeoRS(56), No. 6, June 2018, pp. 3185-3195.
IEEE DOI
1806
Clustering algorithms, Hyperspectral imaging, Laplace equations,
Manifolds, Matrix decomposition, Sparse matrices, Band selection,
structured random projection (SRP)
BibRef
Zhang, W.Q.[Wen-Qiang],
Li, X.R.[Xiao-Run],
Dou, Y.X.[Ya-Xing],
Zhao, L.Y.[Liao-Ying],
A Geometry-Based Band Selection Approach for Hyperspectral Image
Analysis,
GeoRS(56), No. 8, August 2018, pp. 4318-4333.
IEEE DOI
1808
feature extraction, geophysical image processing,
geophysical techniques, hyperspectral imaging,
sequential forward search (SFS)
BibRef
Xie, F.D.[Fu-Ding],
Li, F.F.[Fang-Fei],
Lei, C.K.[Cun-Kuan],
Ke, L.[Lina],
Representative Band Selection for Hyperspectral Image Classification,
IJGI(7), No. 9, 2018, pp. xx-yy.
DOI Link
1810
BibRef
Wang, Q.[Qi],
Zhang, F.H.[Fa-Hong],
Li, X.L.[Xue-Long],
Optimal Clustering Framework for Hyperspectral Band Selection,
GeoRS(56), No. 10, October 2018, pp. 5910-5922.
IEEE DOI
1810
Correlation, Linear programming, Hyperspectral imaging,
Clustering algorithms, Covariance matrices, Noise measurement,
spectral clustering (SC)
BibRef
Wang, Q.[Qi],
Zhang, F.H.[Fa-Hong],
Li, X.L.[Xue-Long],
Hyperspectral Band Selection via Optimal Neighborhood Reconstruction,
GeoRS(58), No. 12, December 2020, pp. 8465-8476.
IEEE DOI
2012
Correlation, Optimization, Linear programming,
Hyperspectral imaging, Image reconstruction, Feature extraction,
sparse representation
BibRef
Fontanella, A.[Alessandro],
Marenzi, E.[Elisa],
Torti, E.[Emanuele],
Danese, G.[Giovanni],
Plaza, A.[Antonio],
Leporati, F.[Francesco],
A suite of parallel algorithms for efficient band selection from
hyperspectral images,
RealTimeIP(14), No. 3, October 2018, pp. 537-553.
Springer DOI
1811
BibRef
Cao, X.H.[Xiang-Hai],
Ji, Y.[Yamei],
Wang, L.[Lin],
Ji, B.B.[Bei-Bei],
Jiao, L.C.[Li-Cheng],
Han, J.G.[Jun-Gong],
Fast hyperspectral band selection based on spatial feature extraction,
RealTimeIP(14), No. 3, October 2018, pp. 555-564.
Springer DOI
1811
BibRef
Wei, X.,
Zhu, W.,
Liao, B.,
Cai, L.,
Matrix-Based Margin-Maximization Band Selection With Data-Driven
Diversity for Hyperspectral Image Classification,
GeoRS(56), No. 12, December 2018, pp. 7294-7309.
IEEE DOI
1812
Feature extraction, Fasteners, Hyperspectral imaging, Training,
Metals, Soil, Adaptive similarity learning,
matrix-based hinge loss function
BibRef
Zhang, W.Q.[Wen-Qiang],
Li, X.R.[Xiao-Run],
Zhao, L.Y.[Liao-Ying],
Band Priority Index:
A Feature Selection Framework for Hyperspectral Imagery,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
BibRef
Li, Q.A.[Qi-Ang],
Wang, Q.[Qi],
Li, X.L.[Xue-Long],
An Efficient Clustering Method for Hyperspectral Optimal Band
Selection via Shared Nearest Neighbor,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Wei, X.,
Zhu, W.,
Liao, B.,
Cai, L.,
Scalable One-Pass Self-Representation Learning for Hyperspectral Band
Selection,
GeoRS(57), No. 7, July 2019, pp. 4360-4374.
IEEE DOI
1907
Hyperspectral imaging, Principal component analysis, Big Data,
Matrix decomposition, Sensors, Adaptive regression, classification,
row-sparsity norm
BibRef
Yu, C.,
Wang, Y.,
Song, M.,
Chang, C.,
Class Signature-Constrained Background- Suppressed Approach to Band
Selection for Classification of Hyperspectral Images,
GeoRS(57), No. 1, January 2019, pp. 14-31.
IEEE DOI
1901
Hyperspectral imaging, Array signal processing, Correlation,
Search problems, Training,
linearly constrained minimum variance (LCMV)
BibRef
Jiang, X.F.[Xue-Feng],
Zhang, L.[Lin],
Liu, J.R.[Jun-Rui],
Li, S.Y.[Shu-Ying],
Maximum simplex volume: an efficient unsupervised band selection method
for hyperspectral image,
IET-CV(13), No. 2, March 2019, pp. 233-239.
DOI Link
1902
BibRef
Zhang, A.Z.[Ai-Zhu],
Ma, P.[Ping],
Liu, S.[Sihan],
Sun, G.Y.[Gen-Yun],
Huang, H.[Hui],
Zabalza, J.[Jaime],
Wang, Z.J.[Zhen-Jie],
Lin, C.Y.[Cheng-Yan],
Hyperspectral band selection using crossover-based gravitational search
algorithm,
IET-IPR(13), No. 2, February 2019, pp. 280-286.
DOI Link
1902
BibRef
Yu, W.B.[Wen-Bo],
Zhang, M.[Miao],
Shen, Y.[Yi],
Combined FATEMD-based band selection method for hyperspectral images,
IET-IPR(13), No. 2, February 2019, pp. 287-298.
DOI Link
1902
BibRef
Hashjin, S.S.[Shahram Sharifi],
Boloorani, A.D.[Ali Darvishi],
Khazai, S.[Safa],
Kakroodi, A.A.[Ata Abdollahi],
Selecting optimal bands for sub-pixel target detection in hyperspectral
images based on implanting synthetic targets,
IET-IPR(13), No. 2, February 2019, pp. 323-331.
DOI Link
1902
BibRef
Jin, J.[Jia],
Wang, Q.[Quan],
Evaluation of Informative Bands Used in Different PLS Regressions for
Estimating Leaf Biochemical Contents from Hyperspectral Reflectance,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Patro, R.N.[Ram Narayan],
Subudhi, S.[Subhashree],
Biswal, P.K.[Pradyut Kumar],
Spectral clustering and spatial Frobenius norm-based Jaya optimisation
for BS of hyperspectral images,
IET-IPR(13), No. 2, February 2019, pp. 307-315.
DOI Link
1902
BibRef
Zhai, H.,
Zhang, H.,
Zhang, L.,
Li, P.,
Laplacian-Regularized Low-Rank Subspace Clustering for Hyperspectral
Image Band Selection,
GeoRS(57), No. 3, March 2019, pp. 1723-1740.
IEEE DOI
1903
eigenvalues and eigenfunctions, feature extraction,
hyperspectral imaging, image classification,
sparse representation (SR)
BibRef
Wang, Y.,
Wang, L.,
Yu, C.,
Zhao, E.,
Song, M.,
Wen, C.,
Chang, C.,
Constrained-Target Band Selection for Multiple-Target Detection,
GeoRS(57), No. 8, August 2019, pp. 6079-6103.
IEEE DOI
1908
image fusion, minimisation, object detection,
band fusion selection, sequential forward CTBS,
virtual dimensionality (VD)
BibRef
Bevilacqua, M.,
Berthoumieu, Y.,
Multiple-Feature Kernel-Based Probabilistic Clustering for
Unsupervised Band Selection,
GeoRS(57), No. 9, September 2019, pp. 6675-6689.
IEEE DOI
1909
BibRef
Earlier:
Unsupervised hyperspectral band selection via multi-feature
information-maximization clustering,
ICIP17(540-544)
IEEE DOI
1803
Hyperspectral imaging, Probabilistic logic,
Clustering algorithms, Computational modeling,
image representation.
feature extraction, image classification,
pattern clustering, remote sensing, unsupervised learning,
dimensionality reduction
BibRef
Das, S.[Samiran],
Bhattacharya, S.[Shubhobrata],
Routray, A.[Aurobinda],
Deb, A.K.[Alok Kani],
Band selection of hyperspectral image by sparse manifold clustering,
IET-IPR(13), No. 10, 22 August 2019, pp. 1625-1635.
DOI Link
1909
BibRef
Wang, L.[Lin],
Chang, C.I.[Chein-I],
Lee, L.C.[Li-Chien],
Wang, Y.[Yulei],
Xue, B.[Bai],
Song, M.P.[Mei-Ping],
Yu, C.Y.[Chuan-Yan],
Li, S.[Sen],
Band Subset Selection for Anomaly Detection in Hyperspectral Imagery,
GeoRS(55), No. 9, September 2017, pp. 4887-4898.
IEEE DOI
1709
geophysical image processing, hyperspectral imaging,
iterative methods, least squares approximations,
See also Progressive Band Selection of Spectral Unmixing for Hyperspectral Imagery.
BibRef
Lei, J.[Jie],
Xie, W.Y.[Wei-Ying],
Yang, J.[Jian],
Li, Y.S.[Yun-Song],
Chang, C.I.[Chein-I],
Spectral-Spatial Feature Extraction for Hyperspectral Anomaly
Detection,
GeoRS(57), No. 10, October 2019, pp. 8131-8143.
IEEE DOI
1910
feature extraction, hyperspectral imaging, image filtering,
image representation, learning (artificial intelligence),
interference suppression
BibRef
Xie, W.Y.[Wei-Ying],
Li, Y.S.[Yun-Song],
Lei, J.[Jie],
Yang, J.[Jian],
Chang, C.I.[Chein-I],
Li, Z.[Zhen],
Hyperspectral Band Selection for Spectral-Spatial Anomaly Detection,
GeoRS(58), No. 5, May 2020, pp. 3426-3436.
IEEE DOI
2005
Anomaly detection, band selection, hyperspectral image (HSI),
spectral-spatial optimization, unsupervised representation learning
See also Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection.
BibRef
Yu, C.Y.[Chun-Yan],
Song, M.P.[Mei-Ping],
Chang, C.I.[Chein-I],
Band Subset Selection for Hyperspectral Image Classification,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link
1802
BibRef
Song, M.P.[Mei-Ping],
Shang, X.D.[Xiao-Di],
Wang, Y.L.[Yu-Lei],
Yu, C.Y.[Chun-Yan],
Chang, C.I.[Chein-I],
Class Information-Based Band Selection for Hyperspectral Image
Classification,
GeoRS(57), No. 11, November 2019, pp. 8394-8416.
IEEE DOI
1911
Integrated circuits, Training, Hyperspectral imaging, Entropy,
Signal to noise ratio, Information theory, Band selection (BS),
within class distance (WCD)
BibRef
Chang, C.I.[Chein-I],
Kuo, Y.M.[Yi-Mei],
Ma, K.Y.[Kenneth Yeonkong],
Band Selection via Band Density Prominence Clustering for
Hyperspectral Image Classification,
RS(16), No. 6, 2024, pp. 942.
DOI Link
2403
BibRef
Li, J.H.[Jin-Hui],
Li, X.R.[Xiao-Run],
Chen, S.H.[Shu-Han],
HyperBT: Redundancy Reduction-Based Self-Supervised Learning for
Hyperspectral Image Classification,
SPLetters(31), 2024, pp. 2385-2389.
IEEE DOI
2410
Feature extraction, Redundancy, Data mining, Correlation, Training,
Hyperspectral imaging, Data augmentation, HyperBT,
spatial-spectral feature
BibRef
Chang, C.I.[Chein-I],
Kuo, Y.M.[Yi-Mei],
Chen, S.H.[Shu-Han],
Liang, C.C.[Chia-Chen],
Ma, K.Y.[Kenneth Yeonkong],
Hu, P.F.M.[Peter Fu-Ming],
Self-Mutual Information-Based Band Selection for Hyperspectral Image
Classification,
GeoRS(59), No. 7, July 2021, pp. 5979-5997.
IEEE DOI
2106
Hyperspectral imaging, Entropy, Correlation,
Extraterrestrial measurements, Probability distribution, Sensors,
virtual dimensionality (VD)
BibRef
Wang, Y.[Yulei],
Wang, L.[Lin],
Xie, H.Y.[Hong-Ye],
Chang, C.I.[Chein-I],
Fusion of Various Band Selection Methods for Hyperspectral Imagery,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Hennessy, A.[Andrew],
Clarke, K.[Kenneth],
Lewis, M.[Megan],
Hyperspectral Classification of Plants:
A Review of Waveband Selection Generalisability,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link
2001
BibRef
Sui, C.,
Li, C.,
Feng, J.,
Mei, X.,
Unsupervised Manifold-Preserving and Weakly Redundant Band Selection
Method for Hyperspectral Imagery,
GeoRS(58), No. 2, February 2020, pp. 1156-1170.
IEEE DOI
2001
Manifolds, Measurement, Hyperspectral imaging, Redundancy,
Optimization, Correlation, Band-weight optimization,
redundancy
BibRef
Cai, Y.,
Liu, X.,
Cai, Z.,
BS-Nets: An End-to-End Framework for Band Selection of Hyperspectral
Image,
GeoRS(58), No. 3, March 2020, pp. 1969-1984.
IEEE DOI
2003
Hyperspectral imaging, Image reconstruction, Neural networks,
Geology, Feature extraction, Task analysis, Attention mechanism,
spectral reconstruction
BibRef
Xie, W.,
Lei, J.,
Yang, J.,
Li, Y.,
Du, Q.,
Li, Z.,
Deep Latent Spectral Representation Learning-Based Hyperspectral Band
Selection for Target Detection,
GeoRS(58), No. 3, March 2020, pp. 2015-2026.
IEEE DOI
2003
Hyperspectral imaging, Feature extraction, Object detection,
Optimization, Principal component analysis, Noise measurement,
spectral consistency
See also Hyperspectral Band Selection for Spectral-Spatial Anomaly Detection.
BibRef
Chen, W.Z.[Wei-Zhao],
Yang, Z.J.[Zhi-Jing],
Ren, J.[JinChang],
Cao, J.Z.[Jiang-Zhong],
Cai, N.[Nian],
Zhao, H.M.[Hui-Min],
Yuen, P.[Peter],
MIMN-DPP: Maximum-information and minimum-noise determinantal point
processes for unsupervised hyperspectral band selection,
PR(102), 2020, pp. 107213.
Elsevier DOI
2003
Hyperspectral images (HSI), Unsupervised band selection,
Maximum information and minimum noise (MIMN) criterion,
Determinantal point processes (DPP),
BibRef
Wei, X.,
Cai, L.,
Liao, B.,
Lu, T.,
Local-View-Assisted Discriminative Band Selection With Hypergraph
Autolearning for Hyperspectral Image Classification,
GeoRS(58), No. 3, March 2020, pp. 2042-2055.
IEEE DOI
2003
Hyperspectral imaging, Training, Indexes, Robustness, Fasteners,
Feature extraction, Auto-learning hypergraph,
supervised band selection (BS)
BibRef
Sun, W.,
Peng, J.,
Yang, G.,
Du, Q.,
Fast and Latent Low-Rank Subspace Clustering for Hyperspectral Band
Selection,
GeoRS(58), No. 6, June 2020, pp. 3906-3915.
IEEE DOI
2005
Band selection, correntropy measure, hyperspectral imagery (HSI),
latent low-rank subspace clustering, remote sensing
BibRef
Su, P.F.[Pei-Feng],
Tarkoma, S.[Sasu],
Pellikka, P.K.E.[Petri K. E.],
Band Ranking via Extended Coefficient of Variation for Hyperspectral
Band Selection,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
He, C.L.[Chun-Lin],
Zhang, Y.[Yong],
Gong, D.[Dunwei],
A Pseudo-Label Guided Artificial Bee Colony Algorithm for
Hyperspectral Band Selection,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Vaddi, R.[Radhesyam],
Manoharan, P.[Prabukumar],
Hyperspectral remote sensing image classification using combinatorial
optimisation based un-supervised band selection and CNN,
IET-IPR(14), No. 15, 15 December 2020, pp. 3909-3919.
DOI Link
2103
BibRef
Geng, X.R.[Xiu-Rui],
Wang, L.[Lei],
Ji, L.[Luyan],
Identify Informative Bands for Hyperspectral Target Detection Using
the Third-Order Statistic,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link
2105
BibRef
Wang, Q.[Qi],
Li, Q.[Qiang],
Li, X.L.[Xue-Long],
A Fast Neighborhood Grouping Method for Hyperspectral Band Selection,
GeoRS(59), No. 6, June 2021, pp. 5028-5039.
IEEE DOI
2106
Hyperspectral imaging, Clustering algorithms,
Partitioning algorithms, Information entropy, Complexity theory,
neighborhood grouping
BibRef
Feng, J.[Jie],
Chen, J.T.[Jian-Tong],
Sun, Q.G.[Qi-Gong],
Shang, R.H.[Rong-Hua],
Cao, X.H.[Xiang-Hai],
Zhang, X.R.[Xiang-Rong],
Jiao, L.C.[Li-Cheng],
Convolutional Neural Network Based on Bandwise-Independent
Convolution and Hard Thresholding for Hyperspectral Band Selection,
Cyber(51), No. 9, September 2021, pp. 4414-4428.
IEEE DOI
2109
Convolution, Feature extraction, Training, Support vector machines,
Kernel, Hyperspectral imaging, Standards, 3-D dilated convolution,
straight-through estimator (STE)
BibRef
Liu, Y.F.[Yu-Fei],
Li, X.R.[Xiao-Run],
Hua, Z.Q.[Zi-Qiang],
Zhao, L.Y.[Liao-Ying],
EBARec-BS: Effective Band Attention Reconstruction Network for
Hyperspectral Imagery Band Selection,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Qi, J.H.[Jia-Hao],
Gong, Z.Q.[Zhi-Qiang],
Yao, A.[Aihuan],
Liu, X.Y.[Xing-Yue],
Li, Y.Q.[Yong-Qian],
Zhang, Y.C.[Yi-Chuang],
Zhong, P.[Ping],
Bathymetric-Based Band Selection Method for Hyperspectral Underwater
Target Detection,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link
2110
BibRef
Zhao, L.[Lin],
Yi, J.W.[Jia-Wen],
Li, X.[Xi],
Hu, W.J.[Wen-Jing],
Wu, J.H.[Jian-Hui],
Zhang, G.[Guoyun],
Compact Band Weighting Module Based on Attention-Driven for
Hyperspectral Image Classification,
GeoRS(59), No. 11, November 2021, pp. 9540-9552.
IEEE DOI
2111
Feature extraction, Support vector machines, Correlation,
Task analysis, Performance evaluation, Hyperspectral imaging,
lightweight module
BibRef
Xu, B.[Buyun],
Li, X.[Xihai],
Hou, W.J.[Wei-Jun],
Wang, Y.T.[Yi-Ting],
Wei, Y.W.[Yi-Wei],
A Similarity-Based Ranking Method for Hyperspectral Band Selection,
GeoRS(59), No. 11, November 2021, pp. 9585-9599.
IEEE DOI
2111
Hyperspectral imaging, Indexes, Training, Partitioning algorithms,
Feature extraction, Euclidean distance, Ellipsoids,
similarity measurement
BibRef
Wang, W.G.[Wen-Guang],
Wang, W.H.[Wen-Hong],
Liu, H.F.[Hong-Fu],
Correlation-Guided Ensemble Clustering for Hyperspectral Band
Selection,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link
2203
BibRef
Jang, W.[Wonjin],
Park, Y.[Yongeun],
Pyo, J.[JongCheol],
Park, S.[Sanghyun],
Kim, J.[Jinuk],
Kim, J.H.[Jin Hwi],
Cho, K.H.[Kyung Hwa],
Shin, J.K.[Jae-Ki],
Kim, S.[Seongjoon],
Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve
a Wide Range of Cyanobacterial Pigment Concentration Using a
Data-Driven Approach,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Sun, H.[He],
Zhang, L.[Lei],
Ren, J.C.[Jin-Chang],
Huang, H.[Hua],
Novel hyperbolic clustering-based band hierarchy (HCBH) for effective
unsupervised band selection of hyperspectral images,
PR(130), 2022, pp. 108788.
Elsevier DOI
2206
Hyperspectral image, Unsupervised band selection,
Hyperbolic space clustering, Hierarchical clustering
BibRef
Sun, H.[He],
Zhang, L.[Lei],
Wang, L.Z.[Li-Zhi],
Huang, H.[Hua],
Stochastic gate-based autoencoder for unsupervised hyperspectral band
selection,
PR(132), 2022, pp. 108969.
Elsevier DOI
2209
Hyperspectral data, Unsupervised band selection, Autoencoder, Stochastic gate
BibRef
Li, S.Y.[Shu-Ying],
Peng, B.D.[Bai-Dong],
Fang, L.[Long],
Li, Q.[Qiang],
Hyperspectral Band Selection via Optimal Combination Strategy,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Han, X.Z.[Xi-Zhen],
Jiang, Z.G.[Zhen-Gang],
Liu, Y.Y.[Yuan-Yuan],
Zhao, J.[Jian],
Sun, Q.[Qiang],
Li, Y.Z.[Ying-Zhi],
A Spatial-Spectral Combination Method for Hyperspectral Band
Selection,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Yuan, S.X.[Shao-Xiong],
Song, G.M.[Guang-Man],
Huang, G.Q.[Guang-Qing],
Wang, Q.[Quan],
Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN
Model to Classify Plant Species from Reflectance,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Deng, C.Z.[Cheng-Zhi],
Chen, Y.G.[Yong-Gang],
Zhang, S.Q.[Shao-Quan],
Li, F.[Fan],
Lai, P.F.[Peng-Fei],
Su, D.[Dingli],
Hu, M.[Min],
Wang, S.Q.[Sheng-Qian],
Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed
Hyperspectral Imagery,
RS(15), No. 16, 2023, pp. 4056.
DOI Link
2309
BibRef
You, M.[Mengbo],
Meng, X.C.[Xian-Cheng],
Wang, Y.[Yishu],
Jin, H.Y.[Hong-Yuan],
Zhai, C.T.[Chun-Ting],
Yuan, A.[Aihong],
Hyperspectral Band Selection via Band Grouping and Adaptive
Multi-Graph Constraint,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Xie, J.Y.[Ji-Yang],
Ma, Z.Y.[Zhan-Yu],
Chang, D.L.[Dong-Liang],
Zhang, G.Q.[Guo-Qiang],
Guo, J.[Jun],
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel
Attention,
PAMI(44), No. 11, November 2022, pp. 8230-8248.
IEEE DOI
2210
WWW Link. Task analysis, Probabilistic logic, Gaussian processes,
Feature extraction, Correlation, Kernel, Visualization, Gaussian process
BibRef
Sun, X.D.[Xu-Dong],
Shen, X.[Xin],
Pang, H.J.[Hui-Juan],
Fu, X.P.[Xian-Ping],
Multiple Band Prioritization Criteria-Based Band Selection for
Hyperspectral Imagery,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Shang, X.D.[Xiao-Di],
Duan, Y.Q.[Yi-Qi],
Wang, X.P.[Xiao-Peng],
Fu, B.J.[Bai-Jia],
Sun, X.D.[Xu-Dong],
Anomaly-background separation and particle swarm optimization based
band selection for hyperspectral anomaly detection,
IET-IPR(18), No. 8, 2024, pp. 2053-2063.
DOI Link
2406
anomaly detection, band selection, hyperspectral image,
particle swarm optimization
BibRef
Habermann, M.[Mateus],
Shiguemori, E.H.[Elcio Hideiti],
Frémont, V.[Vincent],
Unsupervised Cluster-Wise Hyperspectral Band Selection for
Classification,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Deng, C.W.[Chen-Wei],
Jing, D.L.[Dong-Lin],
Ding, Z.H.[Zhi-Han],
Han, Y.Q.[Yu-Qi],
Sparse Channel Pruning and Assistant Distillation for Faster Aerial
Object Detection,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Lin, L.[Lanbo],
Chen, S.J.[Sheng-Jie],
Yang, Y.J.[Yu-Jiu],
Guo, Z.H.[Zhen-Hua],
AACP: Model Compression by Accurate and Automatic Channel Pruning,
ICPR22(2049-2055)
IEEE DOI
2212
Training, Image coding, Computational modeling, Neural networks,
Estimation, Search problems
BibRef
Yang, H.[Hua],
Chen, M.[Ming],
Wu, G.W.[Guo-Wen],
Wang, J.L.[Jia-Li],
Wang, Y.X.[Ying-Xi],
Hong, Z.H.[Zhong-Hua],
Double Deep Q-Network for Hyperspectral Image Band Selection in Land
Cover Classification Applications,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link
2302
BibRef
Zhang, Y.F.[Yi-Fan],
Li, X.[Xu],
Wei, B.G.[Bao-Guo],
Li, L.X.[Li-Xin],
Yue, S.G.[Shi-Gang],
A Fast Hyperspectral Tracking Method via Channel Selection,
RS(15), No. 6, 2023, pp. 1557.
DOI Link
2304
object tracking in hyperspectral video.
BibRef
Ou, X.F.[Xian-Feng],
Wu, M.[Meng],
Tu, B.[Bing],
Zhang, G.[Guoyun],
Li, W.[Wujing],
Multi-Objective Unsupervised Band Selection Method for Hyperspectral
Images Classification,
IP(32), 2023, pp. 1952-1965.
IEEE DOI
2304
Hyperspectral imaging, Optimization, Correlation,
Classification algorithms, Feature extraction, dimensionality reduction
BibRef
Li, Y.[Yuan],
Wu, R.Y.[Ruo-Yu],
Tan, Q.J.[Qi-Juan],
Yang, Z.C.[Zheng-Chun],
Huang, H.[Hong],
Masked Spectral Bands Modeling with Shifted Windows: An Excellent
Self-Supervised Learner for Classification of Medical Hyperspectral
Images,
SPLetters(30), 2023, pp. 543-547.
IEEE DOI
2305
Hyperspectral imaging, Feature extraction, Transformers, Training,
Solid modeling, Signal processing algorithms, shift windows
BibRef
Wang, J.[Jun],
Tang, C.[Chang],
Liu, X.W.[Xin-Wang],
Zhang, W.[Wei],
Li, W.Q.[Wan-Qing],
Zhu, X.Z.[Xin-Zhong],
Wang, L.[Lizhe],
Zomaya, A.Y.[Albert Y.],
Region-Aware Hierarchical Latent Feature Representation
Learning-Guided Clustering for Hyperspectral Band Selection,
Cyber(53), No. 8, August 2023, pp. 5250-5263.
IEEE DOI
2307
Hyperspectral imaging, Feature extraction, Clustering algorithms,
Laplace equations, Information entropy, Clustering methods,
hyperspectral band selection
BibRef
Hu, T.R.[Ting-Rui],
Gao, P.C.[Pei-Chao],
Ye, S.J.[Si-Jing],
Shen, S.[Shi],
Improved SR-SSIM Band Selection Method Based on Band Subspace
Partition,
RS(15), No. 14, 2023, pp. 3596.
DOI Link
2307
BibRef
Liao, B.[Bowen],
Li, Y.X.[Yang-Xincan],
Liu, W.[Wei],
Gao, X.J.[Xian-Jun],
Wang, M.W.[Ming-Wei],
Discarding-Recovering and Co-Evolution Mechanisms Based
Evolutionary Algorithm for Hyperspectral Feature Selection,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Wang, X.Y.[Xian-Yue],
Qian, L.X.[Long-Xia],
Hong, M.[Mei],
Liu, Y.F.[Yi-Fan],
Dual Homogeneous Patches-Based Band Selection Methodology for
Hyperspectral Classification,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link
2308
BibRef
Li, X.R.[Xiao-Run],
Liu, Y.F.[Yu-Fei],
Hua, Z.Q.[Zi-Qiang],
Chen, S.H.[Shu-Han],
An Unsupervised Band Selection Method via Contrastive Learning for
Hyperspectral Images,
RS(15), No. 23, 2023, pp. 5495.
DOI Link
2312
BibRef
Wang, Y.L.[Yu-Lei],
Ma, H.P.[Hai-Peng],
Yang, Y.C.[Yu-Chao],
Zhao, E.[Enyu],
Song, M.P.[Mei-Ping],
Yu, C.Y.[Chun-Yan],
Self-Supervised Deep Multi-Level Representation Learning Fusion-Based
Maximum Entropy Subspace Clustering for Hyperspectral Band Selection,
RS(16), No. 2, 2024, pp. 224.
DOI Link
2402
BibRef
Pesaresi, S.[Simone],
Mancini, A.[Adriano],
Quattrini, G.[Giacomo],
Casavecchia, S.[Simona],
Evaluation and Selection of Multi-Spectral Indices to Classify
Vegetation Using Multivariate Functional Principal Component Analysis,
RS(16), No. 7, 2024, pp. 1224.
DOI Link
2404
BibRef
Cui, C.[Chuanyu],
Sun, X.D.[Xu-Dong],
Fu, B.[Baijia],
Shang, X.D.[Xiao-Di],
SSANet-BS: Spectral-Spatial Cross-Dimensional Attention Network for
Hyperspectral Band Selection,
RS(16), No. 15, 2024, pp. 2848.
DOI Link
2408
BibRef
Zhang, Y.S.[Yong-Shan],
Qi, J.W.[Jian-Wen],
Wang, X.X.[Xin-Xin],
Cai, Z.H.[Zhi-Hua],
Peng, J.T.[Jiang-Tao],
Zhou, Y.C.[Yi-Cong],
Tensorial Global-Local Graph Self-Representation for Hyperspectral
Band Selection,
CirSysVideo(34), No. 12, December 2024, pp. 13213-13225.
IEEE DOI Code:
WWW Link.
2501
Tensors, Correlation, Optimization, Hyperspectral imaging,
Convolution, Clustering algorithms, Adaptation models,
global-local information
BibRef
Li, H.[Hufei],
Cao, J.[Jian],
Liu, X.C.[Xiang-Cheng],
Chen, J.[Jue],
Shang, J.J.[Jing-Jie],
Qian, Y.[Yu],
Wang, Y.[Yuan],
Channel Pruning Via Attention Module And Memory Curve,
ICIP23(1985-1989)
IEEE DOI
2312
BibRef
Dehaeck, S.,
van Belleghem, R.,
Wouters, N.,
de Ketelaere, B.,
Liao, W.,
Optimal Wavelength Selection for Deep Learning from Hyperspectral
Images,
IbPRIA23(249-260).
Springer DOI
2307
BibRef
Li, K.[Ke],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Jointly Learning Band Selection and Filter Array Design for
Hyperspectral Imaging,
WACV23(6373-6383)
IEEE DOI
2302
Training, Image color analysis, Neural networks, Prototypes,
Reinforcement learning, Cameras, Task analysis,
image and video synthesis
BibRef
Yin, S.Z.[Shan-Zhi],
Li, C.[Chao],
Meng, F.Y.[Fan-Yang],
Tan, W.[Wen],
Bao, Y.N.[You-Neng],
Liang, Y.S.[Yong-Sheng],
Liu, W.[Wei],
Exploring Structural Sparsity in Neural Image Compression,
ICIP22(471-475)
IEEE DOI
2211
Training, Adaptation models, Image coding, Convolution,
Computational modeling, Neural networks, Transform coding,
channel pruning
BibRef
Ahishali, M.[Mete],
Kiranyaz, S.[Serkan],
Ahmad, I.[Iftikhar],
Gabbouj, M.[Moncef],
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational
Autoencoder for Hyperspectral Image Band Selection,
ICIP22(2296-2300)
IEEE DOI
2211
Measurement, Neurons, Training data, Data processing, Software,
Band selection, hyperspectral image data, machine learning,
sparse autoencoders
BibRef
Alkhatib, M.Q.,
Velez-Reyes, M.,
Using Band Subset Selection For Dimensionality Reduction In
Superpixel Segmentation Of Hyperspectral Imagery,
ICIP20(26-30)
IEEE DOI
2011
Image segmentation,
Hyperspectral imaging, Dimensionality reduction, Matlab,
Dimensionality reduction.
BibRef
Aldeghlawi, M.,
Velez-Reyes, M.,
A Comparison of Column Subset Selection Methods for Unsupervised Band
Subset Selection in Hyperspectral Imagery,
Southwest18(57-60)
IEEE DOI
1809
Cascading style sheets, Hyperspectral imaging,
Dimensionality reduction, Optimization, Linear algebra, Matlab,
Hyperspectral Imagery
BibRef
Gan, X.,
Liu, J.,
Parallelizing band selection for hyperspectral imagery with
many-threads,
ICIVC17(505-509)
IEEE DOI
1708
Acceleration, Central Processing Unit, Digital signal processing,
Graphics processing units, Hyperspectral imaging,
Synchronization, China accelerator, K-L divergence, band selection,
many-threads
BibRef
hashjin, S.S.[S. Sharifi],
Darvishi, A.,
Khazai, S.,
Hatami, F.,
houtki, M.J.[M. Jafari],
A Band Selection Method For Sub-pixel Target Detection In Hyperspectral
Images Based On Laboratory And Field Reflectance Spectral Comparison,
ISPRS16(B7: 117-120).
DOI Link
1610
BibRef
Le Bris, A.,
Chehata, N.,
Briottet, X.,
Paparoditis, N.,
Extraction of Optimal Spectral Bands Using Hierarchical Band Merging
Out of Hyperspectral Data,
GeoHyper15(459-465).
DOI Link
1602
BibRef
Merzouqi, M.,
Nhaila, H.,
Sarhrouni, E.,
Hammouch, A.,
Improved filter algorithm using inequality fano to select bands for
HSI classification,
ISCV15(1-5)
IEEE DOI
1506
atmospherics
BibRef
Bouchech, H.J.[Hamdi Jamel],
Foufou, S.[Sebti],
Abidi, M.[Mongi],
Multilinear Sparse Decomposition for Best Spectral Bands Selection,
ICISP14(384-391).
Springer DOI
1406
BibRef
Li, H.C.[Hai-Chang],
Wang, Y.[Ying],
Duan, J.Y.[Jiang-Yong],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Group sparsity based semi-supervised band selection for hyperspectral
images,
ICIP13(3225-3229)
IEEE DOI
1402
Band selection;Group sparsity;Hyperspectral imaging;Smoothness prior
BibRef
Bai, J.[Jun],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Classification oriented semi-supervised band selection for
hyperspectral images,
ICPR12(1888-1891).
WWW Link.
1302
BibRef
Li, S.J.[Shuang-Jiang],
Qi, H.R.[Hai-Rong],
Sparse representation based band selection for hyperspectral images,
ICIP11(2693-2696).
IEEE DOI
1201
BibRef
Samadzadegan, F.,
Mahmoudi, F.T.[F. Tabib],
Optimum band selection in hyperspectral imagery using swarm
intelligence optimization algorithms,
ICIIP11(1-6).
IEEE DOI
1112
BibRef
Yao, F.[Futian],
Qian, Y.T.[Yun-Tao],
Band selection based gaussian processes for hyperspectral remote
sensing images classification,
ICIP09(2845-2848).
IEEE DOI
0911
BibRef
Li, X.J.[Xi-Jun],
Liu, J.[Jun],
An adaptive band selection algorithm for dimension reduction of
hyperspectral images,
IASP09(114-118).
IEEE DOI
0904
BibRef
Du, H.T.[Hong-Tao],
Qi, H.R.[Hai-Rong],
Wang, X.L.[Xiao-Ling],
Ramanath, R.,
Snyder, W.E.,
Band selection using independent component analysis for hyperspectral
image processing,
AIPR03(93-98).
IEEE DOI
0310
BibRef
Martínez-Usó, A.[Adolfo],
Pla, F.[Filiberto],
Martínez Sotoca, J.[José],
García-Sevilla, P.[Pedro],
From Narrow to Broad Band Design and Selection in Hyperspectral Images,
ICIAR08(xx-yy).
Springer DOI
0806
BibRef
Earlier:
Comparison of Unsupervised Band Selection Methods for Hyperspectral
Imaging,
IbPRIA07(I: 30-38).
Springer DOI
0706
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly .