14.2.2.2 Hyperspectral Data, Dimensionality Reduction

Bruzzone, L., Serpico, S.B.,
A technique for feature selection in multiclass problems,
JRS(21), No. 3, February 2000, pp. 549. 0002
BibRef

Bruzzone, L.,
An Approach to Feature Selection and Classification of Remote Sensing Images Based on the Bayes Rule for Minimum Cost,
GeoRS(38), No. 1, January 2000, pp. 429-438.
IEEE Top Reference. 0002
BibRef

Serpico, S.B., Bruzzone, L.,
A new search algorithm for feature selection in hyperspectral remote sensing images,
GeoRS(39), No. 7, July 2001, pp. 1360-1367.
IEEE Top Reference. 0108
BibRef

Bruce, L.M., Koger, C.H., Li, J.[Jiang],
Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction,
GeoRS(40), No. 10, October 2002, pp. 2331-2338.
IEEE Top Reference. 0301
BibRef

Kaewpijit, S., Le Moigne, J., El-Ghazawi, T.,
Automatic reduction of hyperspectral imagery using wavelet spectral analysis,
GeoRS(41), No. 4, April 2003, pp. 863-871.
IEEE Abstract. 0307
BibRef

Plaza, A.[Antonio], Martinez, P.[Pablo], Perez, R.[Rosa], Plaza, J.[Javier],
A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles,
PR(37), No. 6, June 2004, pp. 1097-1116.
WWW Version. 0405
BibRef

Plaza, A., Chang, C.I.,
Impact of Initialization on Design of Endmember Extraction Algorithms,
GeoRS(44), No. 11, November 2006, pp. 3397-3407.
IEEE DOI Link 0611
BibRef

Plaza, A.[Antonio], Martinez, P.[Pablo], Plaza, J.[Javier], Perez, R.[Rosa],
Dimensionality Reduction and Classification of Hyperspectral Image Data Using Sequences of Extended Morphological Transformations,
GeoRS(43), No. 3, March 2005, pp. 466-479.
IEEE Abstract. 0501
BibRef

Chang, C.I.[Chein-I], Du, Q.[Qian],
Estimation of number of spectrally distinct signal sources in hyperspectral imagery,
GeoRS(42), No. 3, March 2004, pp. 608-619.
IEEE Abstract. 0407
BibRef

Chang, C.I., Wang, S.,
Constrained Band Selection for Hyperspectral Imagery,
GeoRS(44), No. 6, June 2006, pp. 1575-1585.
IEEE DOI Link 0606
BibRef

Wang, J., Chang, C.I.,
Independent Component Analysis-Based Dimensionality Reduction With Applications in Hyperspectral Image Analysis,
GeoRS(44), No. 6, June 2006, pp. 1586-1600.
IEEE DOI Link 0606
See also Linear Spectral Random Mixture Analysis for Hyperspectral Imagery. BibRef

Wang, J., Chang, C.I.[Chein-I],
Applications of Independent Component Analysis in Endmember Extraction and Abundance Quantification for Hyperspectral Imagery,
GeoRS(44), No. 9, September 2006, pp. 2601-2616.
IEEE DOI Link 0609
BibRef

Chang, C.I.[Chein-I], Jiao, X.L.[Xiao-Li], Wu, C.C.[Chao-Cheng], Du, E.Y., Chen, H.M.[Hsian-Min],
Component Analysis-Based Unsupervised Linear Spectral Mixture Analysis for Hyperspectral Imagery,
GeoRS(49), No. 11, November 2011, pp. 4123-4137.
IEEE DOI Link 1112
BibRef

Chang, C.I.[Chein-I], Wu, C.C., Lo, C.S., Chang, M.L.,
Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction,
GeoRS(48), No. 4, April 2010, pp. 1834-1850.
IEEE DOI Link 1003
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 Link 0703
BibRef

Martínez Sotoca, J.[José], Pla, F.[Filiberto],
Hyperspectral Data Selection from Mutual Information Between Image Bands,
SSPR06(853-861).
Springer DOI Link 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 Link 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 Link 0409
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 Link 0711
BibRef
Earlier: A1, A2, A4, A3:
Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering,
CIARP06(644-654).
Springer DOI Link 0611
BibRef
And: A1, A2, A3, A4:
Clustering-based multispectral band selection using mutual information,
ICPR06(II: 760-763).
IEEE DOI Link 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 Link
WWW Version. 1003
Supervised feature selection; Clustering; Conditional mutual information BibRef

Bandos, T.V., Bruzzone, L., Camps-Valls, G.,
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis,
GeoRS(47), No. 3, March 2009, pp. 862-873.
IEEE DOI Link 0903
BibRef

Camps-Valls, G., Serrano-López, A.J., Gómez-Chova, L., Martín-Guerrero, J.D., Calpe-Maravilla, J., Moreno, J.,
Regularized RBF Networks for Hyperspectral Data Classification,
ICIAR04(II: 429-436).
WWW Version. 0409
BibRef

Zhong, Y., Zhang, L., Huang, B., Li, P.,
An Unsupervised Artificial Immune Classifier for Multi/Hyperspectral Remote Sensing Imagery,
GeoRS(44), No. 2, February 2006, pp. 420-431.
IEEE DOI Link 0602
BibRef

Zhong, Y., Zhang, L., Gong, J., Li, P.,
A Supervised Artificial Immune Classifier for Remote-Sensing Imagery,
GeoRS(45), No. 12, December 2007, pp. 3957-3966.
IEEE DOI Link 0711
BibRef

Zhong, Y., Zhang, L.,
An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery,
GeoRS(50), No. 3, March 2012, pp. 894-909.
IEEE DOI Link 1203
BibRef

Zhang, L., Zhong, Y., Huang, B., Gong, J., Li, P.,
Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery,
GeoRS(45), No. 12, December 2007, pp. 4172-4186.
IEEE DOI Link 0711
BibRef

Zhang, L., Zhang, L., Tao, D., Huang, X.,
On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification,
GeoRS(50), No. 3, March 2012, pp. 879-893.
IEEE DOI Link 1203
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.
WWW Version. 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.
WWW Version. 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 Link 1103
BibRef

Jimenez-Rodriguez, L.O., Arzuaga-Cruz, E., Velez-Reyes, M.,
Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data,
GeoRS(45), No. 2, February 2007, pp. 469-483.
IEEE DOI Link 0703
BibRef

Serpico, S.B., Moser, G.,
Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes,
GeoRS(45), No. 2, February 2007, pp. 484-495.
IEEE DOI Link 0703
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.
WWW Version. 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 Link 0710
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 Link 0711
BibRef

Guo, B.F.[Bao-Feng], Damper, R.I., Gunn, S.R.[Steve R.], Nelson, J.D.B.,
A fast separability-based feature-selection method for high-dimensional remotely sensed image classification,
PR(41), No. 5, May 2008, pp. 1670-1679.
WWW Version. 0711
Feature selection; Mutual information; Remote sensing; Hyperspectral image classification BibRef

Mojaradi, B., Abrishami-Moghaddam, H., Valadan Zoej, M. J., Duin, R.P.W.,
Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction,
GeoRS(47), No. 7, July 2009, pp. 2091-2105.
IEEE DOI Link 0906
BibRef

Yang, J.M.[Jinn-Min], Yu, P.T.[Pao-Ta], Kuo, B.C.[Bor-Chen],
A Nonparametric Feature Extraction and Its Application to Nearest Neighbor Classification for Hyperspectral Image Data,
GeoRS(48), No. 3, March 2010, pp. 1279-1293.
IEEE DOI Link 1003
BibRef

Yang, J.M.[Jinn-Min], Kuo, B.C.[Bor-Chen], Yu, P.T.[Pao-Ta], Chuang, C.H.,
A Dynamic Subspace Method for Hyperspectral Image Classification,
GeoRS(48), No. 7, July 2010, pp. 2840-2853.
IEEE DOI Link 1007
BibRef

Huang, H.Y., Kuo, B.C.,
Double Nearest Proportion Feature Extraction for Hyperspectral-Image Classification,
GeoRS(48), No. 11, November 2010, pp. 4034-4046.
IEEE DOI Link 1011
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 Link 1102
BibRef

Cheng, Q.A.[Qi-Ang], Zhou, H.B.[Hong-Bo], Cheng, J.[Jie],
The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data,
PAMI(33), No. 6, June 2011, pp. 1217-1233.
IEEE DOI Link 1105
Select subset of features in high dimensional data. BibRef

Bai, L.[Liang], Liang, J.[Jiye], Dang, C.[Chuangyin], Cao, F.[Fuyuan],
A novel attribute weighting algorithm for clustering high-dimensional categorical data,
PR(44), No. 12, December 2011, pp. 2843-2861.
Elsevier DOI Link
WWW Version. 1107
Cluster analysis; Optimization algorithm; High-dimensional categorical data; Subspace clustering; Attribute weighting BibRef

Farzam, M., Beheshti, S.,
Simultaneous Denoising and Intrinsic Order Selection in Hyperspectral Imaging,
GeoRS(49), No. 9, September 2011, pp. 3423-3436.
IEEE DOI Link 1109
BibRef

Mei, S.H.[Shao-Hui], He, M.Y.[Ming-Yi], Zhang, Y.[Yifan], Wang, Z.Y.[Zhi-Yong], Feng, D.[Dagan],
Improving Spatial-Spectral Endmember Extraction in the Presence of Anomalous Ground Objects,
GeoRS(49), No. 11, November 2011, pp. 4210-4222.
IEEE DOI Link 1112
BibRef

Li, H.L.[Hua-Li], Zhang, L.P.[Liang-Pei],
A Hybrid Automatic Endmember Extraction Algorithm Based on a Local Window,
GeoRS(49), No. 11, November 2011, pp. 4223-4238.
IEEE DOI Link 1112
BibRef

Chan, T.H.[Tsung-Han], Ma, W.K.[Wing-Kin], Ambikapathi, A., Chi, C.Y.[Chong-Yung],
A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction,
GeoRS(49), No. 11, November 2011, pp. 4177-4193.
IEEE DOI Link 1112
BibRef

Villa, A., Benediktsson, J.A., Chanussot, J., Jutten, C.,
Hyperspectral Image Classification With Independent Component Discriminant Analysis,
GeoRS(49), No. 12, December 2011, pp. 4865-4876.
IEEE DOI Link 1201
BibRef

Liu, J.M.[Jun-Min], Zhang, J.S.[Jiang-She],
A New Maximum Simplex Volume Method Based on Householder Transformation for Endmember Extraction,
GeoRS(50), No. 1, January 2012, pp. 104-118.
IEEE DOI Link 1201
BibRef

Li, G.Z.[Guo-Zheng], Zhao, R.W.[Rui-Wei], Qu, H.N.[Hai-Ni], You, M.Y.[Ming-Yu],
Model selection for partial least squares based dimension reduction,
PRL(33), No. 5, 1 April 2012, pp. 524-529.
Elsevier DOI Link
WWW Version. 1202
Partial least squares; Dimension reduction; Model selection BibRef

Li, W., Prasad, S., Fowler, J.E., Bruce, L.M.,
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis,
GeoRS(50), No. 4, April 2012, pp. 1185-1198.
IEEE DOI Link 1204
BibRef

Ben-David, A.[Avishai], Davidson, C.E.[Charles E.],
Estimation of hyperspectral covariance matrices,
AIPR11(1-4).
IEEE DOI Link 1204
BibRef

Li, S.J.[Shuang-Jiang], Qi, H.R.[Hai-Rong],
Sparse representation based band selection for hyperspectral images,
ICIP11(2693-2696).
IEEE DOI Link 1201
BibRef

Li, W.[Wei], Fowler, J.E.[James E.],
Decoder-side dimensionality determination for compressive-projection principal component analysis of hyperspectral data,
ICIP11(321-324).
IEEE DOI Link 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 Link 1112
BibRef

Datta, A.[Aloke], Ghosh, S.[Susmita], Ghosh, A.[Asish],
Wrapper based feature selection in hyperspectral image data using self-adaptive differential evolution,
ICIIP11(1-6).
IEEE DOI Link 1112
BibRef

Chakrabarti, A.[Ayan], Zickler, T.E.[Todd E.],
Statistics of real-world hyperspectral images,
CVPR11(193-200).
IEEE DOI Link 1106
BibRef

Willson, P.D.[Paul D.], Chan, G.[Gabriel], Yun, P.[Paul],
Vision physiology applied to hyperspectral short wave infrared imaging,
AIPR10(1-3).
IEEE DOI Link 1010
Means of reducing hyperspectral feature space to a multispectral feature space that is orthogonal and optimal. BibRef

Mehdizadeh, M.[Maryam], MacNish, C.[Cara], Khan, R.N.[R. Nazim], Bennamoun, M.[Mohammed],
Semi-supervised Neighborhood Preserving Discriminant Embedding: A Semi-supervised Subspace Learning Algorithm,
ACCV10(III: 199-212).
Springer DOI Link 1011
BibRef

Wen, J.H.[Jin-Huan], Tian, Z.[Zheng], She, H.W.[Hong-Wei], Yan, W.D.[Wei-Dong],
Feature extraction of hyperspectral images based on preserving neighborhood discriminant embedding,
IASP10(257-262).
IEEE DOI Link 1004
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 Link 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 Link 0904
BibRef

Lee, C., Choi, E., Choe, J., Jeong, T.,
Dimension Reduction and Pre-emphasis for Compression of Hyperspectral Images,
ICIAR04(II: 446-453).
WWW Version. 0409
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 Link 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 Link 0806
BibRef
Earlier:
Comparison of Unsupervised Band Selection Methods for Hyperspectral Imaging,
IbPRIA07(I: 30-38).
Springer DOI Link 0706
BibRef

Berge, A.[Asbjørn], Solberg, A.S.[Anne Schistad],
Improving Hyperspectral Classifiers: The Difference Between Reducing Data Dimensionality and Reducing Classifier Parameter Complexity,
SCIA07(293-302).
Springer DOI Link 0706
BibRef

Marçal, A.R.S.[André R.S.], Borges, J.S.[Janete S.],
Estimating the Natural Number of Classes on Hierarchically Clustered Multi-spectral Images,
ICIAR05(447-455).
Springer DOI Link 0509
BibRef

Zeng, H.W.[Hui-Wen], Trussell, H.J.,
Feature Selection using a Mixed-Norm Penalty Function,
ICIP06(997-1000). 0610

IEEE DOI Link BibRef
Earlier:
Dimensionality reduction in hyperspectral image classification,
ICIP04(II: 913-916).
IEEE DOI Link 0505
BibRef

Muhammed, H.H., Ammenberg, P., Bengtsson, E.,
Using feature-vector based analysis, based on principal component analysis and independent component analysis, for analysing hyperspectral images,
CIAP01(309-315).
IEEE Top Reference. 0210
BibRef

Zhang, Y.[Ye], Desai, M.D.[Mita D.],
Adaptive Subspace Decomposition for Hyperspectral Data Dimensionality Reduction,
ICIP99(II:326-329).
IEEE Abstract. BibRef 9900

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly .