14.2.2.4.5 Hyperspectral Data Band Selection

Chapter Contents (Back)
Hyperspectral. Band Selection.

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.
DOI Link 1804
BibRef

Martínez Sotoca, J.[José], Pla, F., Salvador Sánchez, J.,
Band Selection in Multispectral Images by Minimization of Dependent Information,
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 Bands,
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], Pla, F., Klaren, A.C.,
Unsupervised band selection for multispectral images using information theory,
ICPR04(III: 510-513).
IEEE DOI 0409
BibRef

Ball, J.E., Bruce, L.M.,
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], Pla, F.[Filiberto], Martínez Sotoca, J.[José], García-Sevilla, P.[Pedro],
Clustering-Based Hyperspectral Band Selection Using Information Measures,
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 information-based distances,
PR(43), No. 6, June 2010, pp. 2068-2081.
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. BibRef

Chen, G., Qian, S.E.,
Dimensionality reduction of hyperspectral imagery using improved locally linear embedding,
AppRS(1), 2007, pp. 013509. BibRef 0700

Chen, G., Qian, S.E.,
Evaluation and comparison of dimensionality reduction techniques and band selection,
CanRS(34), No. 1, 2008, pp. 26-36. BibRef 0800

Chen, G., Qian, S.E.,
Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis,
JRS(30), No. 18, 2009, pp. 4889-4895, 2009. BibRef 0900

Chen, G., Qian, S.E.,
Simultaneous dimensionality reduction and denoising of yperspectral imagery using bivariate wavelet shrinking and PCA,
CanRS(34), No. 5, 2008, pp. 447-454, 2008. BibRef 0800

Qian, S.E.[Shen-En],
Dimensionality reduction of multidimensional satellite imagery,
SPIE(Newsroom), March 21, 2011.
DOI Link 1103
Novel techniques can reduce dimensionality to derive better remote-sensing products. BibRef

Qian, S.E.[Shen-En],
Enhancing space-based signal-to-noise ratios without redesigning the satellite,
SPIE(Newsroom), January 5, 2011.
DOI Link 1101
A newly developed signal-processing technology based on wavelets can improve the performance of satellite sensors by up to a factor of two. BibRef

Chen, G., Qian, S.E.,
Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage,
GeoRS(49), No. 3, March 2011, pp. 973-980.
IEEE DOI 1103
BibRef

Qian, S.E., Chen, G.,
Enhancing Spatial Resolution of Hyperspectral Imagery Using Sensor's Intrinsic Keystone Distortion,
GeoRS(50), No. 12, December 2012, pp. 5033-5048.
IEEE DOI 1212
BibRef

Vaiphasa, C.[Chaichoke], Skidmore, A.K.[Andrew K.], de Boer, W.F.[Willem F.], Vaiphasa, T.[Tanasak],
A hyperspectral band selector for plant species discrimination,
PandRS(62), No. 3, August 2007, pp. 225-235.
Elsevier DOI 0709
Artificial_Intelligence; Classification; Hyper spectral; Mangrove; Remote sensing; Vegetation BibRef

Wang, S., Chang, C.I.,
Variable-Number Variable-Band Selection for Feature Characterization in Hyperspectral Signatures,
GeoRS(45), No. 9, September 2007, pp. 2979-2992.
IEEE DOI 0710
BibRef

Zhao, Y.Q., Zhang, L., Kong, S.G.,
Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification,
GeoRS(49), No. 2, February 2011, pp. 747-756.
IEEE DOI 1102
BibRef

Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Zhang, X.R.[Xiang-Rong], Sun, T.[Tao],
Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection,
GeoRS(52), No. 7, July 2014, pp. 4092-4105.
IEEE DOI 1403
Approximation methods BibRef

Jia, S.[Sen], Tang, G.H.[Gui-Hua], Zhu, J.[Jiasong], Li, Q.Q.[Qing-Quan],
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection,
GeoRS(54), No. 1, January 2016, pp. 88-102.
IEEE DOI 1601
geophysical image processing BibRef

Zheng, X., Yuan, Y., Lu, X.,
Dimensionality Reduction by Spatial-Spectral Preservation in Selected Bands,
GeoRS(55), No. 9, September 2017, pp. 5185-5197.
IEEE DOI 1709
selected band, spatial-spectral preservation, determinantal point process (DPP), BibRef

Ghaffari, O.[Omid], 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 Analysis,
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.[Junrui], Li, S.[Shuying],
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.[Wenbo], 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, 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

Song, M., Shang, X., Wang, Y., Yu, C., Chang, C.,
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

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

Fu, X.P.[Xian-Ping], Shang, X.D.[Xiao-Di], Sun, X.D.[Xu-Dong], Yu, H.[Haoyang], Song, M.P.[Mei-Ping], Chang, C.I.[Chein-I],
Underwater Hyperspectral Target Detection with Band Selection,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
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.[Peifeng], 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


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 .


Last update:Apr 11, 2021 at 21:43:28