14.2.2.1 High Dimensional Data, Hyperspectral Data, Hyper-Spectral Data Classification

Bryant, J.[Jack],
On the clustering of multidimensional pictorial data,
PR(11), No. 2, 1979, pp. 115-125.
WWW Version. 0309 BibRef

Eden, G., Gelsema, E.S.,
Investigation of multidimensional data using the interactive pattern analysis system ISPAHAN,
PR(11), No. 5-6, 1979, pp. 391-399.
WWW Version. 0309 BibRef

Gelsema, E.S., Eden, G.,
Mapping algorithms in ISPAHAN,
PR(12), No. 3, 1980, pp. 127-136.
WWW Version. 0309 BibRef

Gelsema, E.S., Timmers, T.,
An interactive implementation of nonparametric partitioning in ISPAHAN,
ICPR88(II: 1062-1064).
WWW Version. 8811 BibRef

Curington, I.J.[Ian J.], Cannon, S.E.[Stephen E.],
Multiband image classification with a distributed architecture,
IVC(3), No. 2, May 1985, pp. 80-84.
WWW Version. 0401 BibRef

Chen, C.C.T.[C.C. Thomas], and Landgrebe, D.A.[David A.],
A Spectral Feature Design System for the HIRIS/MODIS Era,
GeoRS(27), No. 6, November 1989, pp. 681-686.
IEEE Top Reference. BibRef 8911

Ismail, M.A., Kamel, M.S.,
Multidimensional data clustering utilizing hybrid search strategies,
PR(22), No. 1, 1989, pp. 75-89.
WWW Version. 0309 BibRef

Zhang, Q.W.[Qi-Wen], Wang, Q.R.[Qen Ring], Boyle, R.D.[Roger D.],
A clustering algorithm for data-sets with a large number of classes,
PR(24), No. 4, 1991, pp. 331-340.
WWW Version. 0401 BibRef

Aeberhard, S.[Stefan], Coomans, D.[Danny], de Vel, O.[Olivier],
Comparative analysis of statistical pattern recognition methods in high dimensional settings,
PR(27), No. 8, August 1994, pp. 1065-1077.
WWW Version. 0401 BibRef

Hoffbeck, J.P., Landgrebe, D.A.,
Classification of Remote Sensing Images Having High Spectral Resolution,
RSE(57), No. 3, September 1996, pp. 119-126. 9609 Hyperspectral. Use the techniques of chemistry spectroscopy for remotely sensed data.
PDF Version. BibRef

Jimenez, L.O.[Luis O.], and Landgrebe, D.A.[David A.],
Supervised Classification in High-Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariate Data,
SMC-C(28), No. 1, February 1998, pp. 39-54. 9806 Hyperspectral.
PDF Version. BibRef

Jimenez, L.O., Landgrebe, D.A.,
Hyperspectral Data Analysis and Supervised Feature Reduction Via Projection Pursuit,
GeoRS(7), No. 6, November 1999, pp. 2653.
IEEE Top Reference. 9911 BibRef

Haertel, V., Landgrebe, D.A.,
On the Classification of Classes with Nearly Equal Spectral Response in Remote Sensing Hyperspectral Image Data,
GeoRS(37), No. 5, September 1999, pp. 2374.
IEEE Top Reference. BibRef 9909

Jackson, Q., Landgrebe, D.A.,
An adaptive classifier design for high-dimensional data analysis with a limited training data set,
GeoRS(39), No. 12, December 2001, pp. 2664-2679.
IEEE Top Reference. 0201 BibRef

Jackson, Q., Landgrebe, D.A.,
An adaptive method for combined covariance estimation and classification,
GeoRS(40), No. 5, May 2002, pp. 1082-1087.
IEEE Top Reference. 0206 BibRef

Nascimento, S.M.C., Ferreira, F., and Foster, D.H.,
Statistics of spatial cone-excitation ratios in natural scenes,
JOSA-A(19), No. 8, August 2002, pp. 1484-1490.
PDF Version. Dataset, Hyperspectral.
HTML Version. BibRef 0208

Foster, D.H., Nascimento, S.M.C., Amano, K., (2004)
Information limits on neural identification of coloured surfaces in natural scenes,
Visual Neuroscience(21), 2004, pp. 331-336.
PDF Version. Dataset, Hyperspectral.
HTML Version. BibRef 0400

Kim, B., and Landgrebe, D.A.,
Hierarchical Classifier Design in High Dimensional, Numerous Class Cases,
GeoRS(29), No. 4, July 1991, pp. 518-528.
IEEE Top Reference. BibRef 9107

Dundar, M.M., Landgrebe, D.A.,
A model-based mixture-supervised classification approach in hyperspectral data analysis,
GeoRS(40), No. 12, December 2002, pp. 2692-2699.
IEEE Top Reference. 0301 BibRef

Madhok, V., Landgrebe, D.A.,
A process model for remote sensing data analysis,
GeoRS(40), No. 3, March 2002, pp. 680-686.
IEEE Top Reference. 0206 BibRef

Kuo, B.C., Landgrebe, D.A.,
A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction,
GeoRS(40), No. 11, November 2002, pp. 2486-2494.
IEEE Top Reference. 0301 BibRef

Dundar, M.M., Landgrebe, D.A.,
A Cost-Effective Semisupervised Classifier Approach With Kernels,
GeoRS(42), No. 1, January 2004, pp. 264-270.
IEEE Abstract. IEEE Top Reference. 0402 BibRef

Dundar, M.M., Landgrebe, D.A.,
Toward an Optimal Supervised Classifier for the Analysis of Hyperspectral Data,
GeoRS(42), No. 1, January 2004, pp. 271-277.
IEEE Abstract. IEEE Top Reference. 0402 BibRef

Nene, S.A.[Sameer A.], Nayar, S.K.[Shree K.],
A Simple Algorithm for Nearest-Neighbor Search in High Dimensions,
PAMI(19), No. 9, September 1997, pp. 989-1003.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9710Find the nearest neighbor only if it is within some distance. Uses projections of the search space. BibRef

Cortijo, F.J., de la Blanca, N.P.[N. Perez],
The performance of regularized discriminant analysis versus non-parametric classifiers applied to high-dimensional image classification,
JRS(20), No. 17, November 1999, pp. 3345. BibRef 9911

Carr, J.R.[James R.], Matanawi, K.[Korblaah],
Correspondence Analysis for Principal Components Transformation of Multispectral and Hyperspectral Digital Images,
PhEngRS(65), No. 8, August 1999, pp. 909. captures 96% of the original image variance in first principal component. BibRef 9908

Benediktsson, J.A., Kanellopoulos, I.,
Classification of Multisource and Hyperspectral Data Based on Decision Fusion,
GeoRS(37), No. 3, May 1999, pp. 1367.
IEEE Top Reference. BibRef 9905

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.,
Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles,
GeoRS(43), No. 3, March 2005, pp. 480-491.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

Jimenez, L.O., Morales-Morell, A., Creus, A.,
Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and Neural Networks,
GeoRS(37), No. 3, May 1999, pp. 1360.
IEEE Top Reference. BibRef 9905

Ifarraguerri, A., Chang, C.I.,
Multispectral and Hyperspectral Image Analysis with Convex Cones,
GeoRS(37), No. 2, March 1999, pp. 756.
IEEE Top Reference. BibRef 9903

Ifarraguerri, A., Chang, C.I.[Chein-I],
Unsupervised Hyperspectral Image Analysis with Projection Pursuit,
GeoRS(38), No. 6, November 2000, pp. 2529-2538.
IEEE Top Reference. 0011 BibRef

Chang, C.I.[Chein-I],
Hyperspectral Imaging: Techniques for Spectral Detection and Classification,
2004, ISBN:0-306-47483-2.
WWW Version. BibRef 0000

Tu, T.M.[Te-Ming], Shyu, H.C.[Hsuen-Chyun], Lee, C.H.[Ching-Hai], Chang, C.I.[Chein-I],
An oblique subspace projection approach for mixed pixel classification in hyperspectral images,
PR(32), No. 8, August 1999, pp. 1399-1408.
WWW Version. See also Anomaly detection and classification for hyperspectral imagery. BibRef 9908

Pesses, M.E.,
Least-Squares-Filter Vector Hybrid Approach to Hyperspectral Subpixel Demixing,
GeoRS(37), No. 2, March 1999, pp. 846.
IEEE Top Reference. BibRef 9903

McGwire, K.[Kenneth], Minor, T.[Timothy], Fenstermaker, L.[Lynn],
Hyperspectral Mixture Modeling for Quantifying Sparse Vegetation Cover in Arid Environments,
RSE(72), No. 3, 2000, pp. 360-374. 0005 BibRef

Schweizer, S.M., Moura, J.M.F.,
Efficient detection in hyperspectral imagery,
IP(10), No. 4, April 2001, pp. 584-597.
WWW Version. 0104 BibRef

Nielsen, A.A.[Allan Aasbjerg],
Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data,
IJCV(42), No. 1-2, April-May 2001, pp. 17-37.
WWW Version. 0106 BibRef
And: JMIV(15), No. 1/2, July 2001, pp. 17-37.
WWW Version. 0106 BibRef

Nielsen, A.A.,
Linear Mixture Models, Full and Partial Unmixing in Multi- and Hyperspectral Image Data,
SCIA99(Remote Sensing). BibRef 9900

Landgrebe, D.A., Serpico, S.B., Crawford, M.M., Singhroy, V.,
Introduction to the special issue on analysis of hyperspectral image data,
GeoRS(39), No. 7, July 2001, pp. 1343-1345.
IEEE Top Reference. 0108 BibRef

Healey, G., Slater, D.A.,
Models and Methods for Automated Material Indentification in Hyperspectral Imagery Acquired under Unknown Illumination and Atmospheric Conditions,
GeoRS(37), No. 6, November 1999, pp. 2707-2717.
IEEE Top Reference. BibRef 9911

Suen, P., Healey, G., Slater, D.A.,
The impact of viewing geometry on material discriminability in hyperspectral images,
GeoRS(39), No. 7, July 2001, pp. 1352-1359.
IEEE Top Reference. 0108 BibRef

Kumar, S., Ghosh, J., Crawford, M.M.,
Best-bases feature extraction algorithms for classification of hyperspectral data,
GeoRS(39), No. 7, July 2001, pp. 1368-1379.
IEEE Top Reference. 0108Generalized Local Discriminant Bases BibRef

Ham, J., Chen, Y., Crawford, M.M., Ghosh, J.,
Investigation of the Random Forest Framework for Classification of Hyperspectral Data,
GeoRS(43), No. 3, March 2005, pp. 492-501.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

Rajan, S., Ghosh, J., Crawford, M.M.,
Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data,
GeoRS(44), No. 11, November 2006, pp. 3408-3417.
WWW Version. 0611 BibRef

Rajan, S., Ghosh, J., Crawford, M.M.,
An Active Learning Approach to Hyperspectral Data Classification,
GeoRS(46), No. 4, April 2008, pp. 1231-1242.
WWW Version. 0803 BibRef

Funk, C.C., Theiler, J., Roberts, D.A., Borel, C.C.,
Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery,
GeoRS(39), No. 7, July 2001, pp. 1410-1420.
IEEE Top Reference. 0108 BibRef

Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., Pippi, I.,
Information-theoretic assessment of sampled hyperspectral imagers,
GeoRS(39), No. 7, July 2001, pp. 1447-1458.
IEEE Top Reference. 0108 BibRef

Lewis, M., Jooste, V., de Gasparis, A.A.,
Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery,
GeoRS(39), No. 7, July 2001, pp. 1471-1479.
IEEE Top Reference. 0108 BibRef

Garcia, M., Ustin, S.L.,
Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in california,
GeoRS(39), No. 7, July 2001, pp. 1480-1490.
IEEE Top Reference. 0108 BibRef

Tsai, F.[Fuan], Philpot, W.D.,
A derivative-aided hyperspectral image analysis system for land-cover classification,
GeoRS(40), No. 2, February 2002, pp. 416-425.
IEEE Top Reference. 0205 BibRef

Thai, B.[Bea], Healey, G.[Glenn],
Invariant subpixel material detection in hyperspectral imagery,
GeoRS(40), No. 3, March 2002, pp. 599-608.
IEEE Top Reference. 0206 BibRef
And:
Invariant Subpixel Material Identification in Hyperspectral Imagery,
DARPA98(809-814). BibRef
Earlier:
Using a Linear Subspace Approach for Invariant Subpixel Material Identification in Airborne Hyperspectral Imagery,
CVPR99(I: 567-572).
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef

Jia, X.P.[Xiu-Ping], Richards, J.A.,
Cluster-space representation for hyperspectral data classification,
GeoRS(40), No. 3, March 2002, pp. 593-598.
IEEE Top Reference. 0206 BibRef

Jia, X.P.[Xiu-Ping], Richards, J.A.,
Efficient transmission and classification of hyperspectral image data,
GeoRS(41), No. 5, May 2003, pp. 1129-1131.
IEEE Abstract. IEEE Top Reference. 0307 BibRef

Holden, H.[Heather], LeDrew, E.[Ellsworth],
Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment,
RSE(81), No. 2-3, August 2002, pp. 300-308.
HTML Version. 0206 BibRef

Bakker, W.H., Schmidt, K.S.,
Hyperspectral edge filtering for measuring homogeneity of surface cover types,
PandRS(56), No. 4, July 2002, pp. 246-256.
HTML Version. 0207 BibRef

Priebe, C.E.[Carey E.], Marchette, D.J.[David J.],
Adaptive mixture density estimation,
PR(26), No. 5, May 1993, pp. 771-785.
WWW Version. 0401 BibRef

Priebe, C.E.[Carey E.], Marchette, D.J.[David J.],
Adaptive mixtures: Recursive nonparametric pattern recognition,
PR(24), No. 12, 1991, pp. 1197-1209.
WWW Version. 0401 BibRef

Marchette, D.J.[David J.], Priebe, C.E.[Carey E.],
Characterizing the scale dimension of a high-dimensional classification problem,
PR(36), No. 1, January 2003, pp. 45-60.
WWW Version. 0210 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. IEEE Top Reference. 0307 BibRef

Baltsavias, E.P.[Emmanuel P.],
Special section on Image Spectroscopy and Hyperspectral Imaging,
PandRS(57), No. 3, December 2002, pp. 169-170.
WWW Version. 0307 BibRef

Staenz, K., Secker, J., Gao, B.C., Davis, C., Nadeau, C.,
Radiative transfer codes applied to hyperspectral data for the retrieval of surface reflectance,
PandRS(57), No. 3, December 2002, pp. 194-203.
WWW Version. 0307 BibRef

Rahman, A.F.[Abdullah F.], Gamon, J.A.[John A.], Sims, D.A.[Daniel A.], Schmidts, M.[Miriam],
Optimum pixel size for hyperspectral studies of ecosystem function in southern California chaparral and grassland,
RSE(84), No. 2, February 2003, pp. 192-207.
WWW Version. 0309 BibRef

Verhoef, W.[Wout], Bach, H.[Heike],
Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models,
RSE(87), No. 1, 15 September 2003, pp. 23-41.
WWW Version. 0309 BibRef

Guo, D.[Diansheng], Peuquet, D.J.[Donna J.], Gahegan, M.[Mark],
ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata,
GeoInfo(7), No. 3, September 2003, pp. 229-253.
WWW Version. 0309 BibRef

Bachmann, C.M.,
Improving the performance of classifiers in high-dimensional remote sensing applications: an adaptive resampling strategy for error-prone exemplars (ARESEPE),
GeoRS(41), No. 9, September 2003, pp. 2101-2112.
IEEE Abstract. IEEE Top Reference. 0310 BibRef

Paclík, P.[Pavel], Duin, R.P.W.[Robert P. W.],
Dissimilarity-based classification of spectra: computational issues,
RealTimeImg(9), No. 4, August 2003, pp. 237-244.
WWW Version.
PDF Version. 0311 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.[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. IEEE Top Reference. 0501 BibRef

Plaza, A., Chang, C.I.,
Impact of Initialization on Design of Endmember Extraction Algorithms,
GeoRS(44), No. 11, November 2006, pp. 3397-3407.
WWW Version. 0611 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. IEEE Top Reference. 0407 BibRef

Chang, C.I., Wang, S.,
Constrained Band Selection for Hyperspectral Imagery,
GeoRS(44), No. 6, June 2006, pp. 1575-1585.
WWW Version. 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.
WWW Version. 0606 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.
WWW Version. 0609 BibRef

Nascimento, J.M.P., Dias, J.M.B.,
Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?,
GeoRS(43), No. 1, January 2005, pp. 175-187.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

Nascimento, J.M.P., Bioucas-Dias, J.M.B.,
Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data,
GeoRS(43), No. 4, April 2005, pp. 898-910.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

Nascimento, J.M.P.[José M. P.], Bioucas-Dias, J.M.B.[José M.B.],
Dependent Component Analysis: A Hyperspectral Unmixing Algorithm,
IbPRIA07(II: 612-619).
WWW Version. 0706 BibRef

Borges, J.S.[Janete S.], Bioucas-Dias, J.M.B.[José M.B.], Marçal, A.R.S.[André R. S.],
Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning,
IbPRIA07(I: 22-29).
WWW Version. 0706 BibRef

Bachmann, C.M., Ainsworth, T.L., Fusina, R.A.,
Exploiting Manifold Geometry in Hyperspectral Imagery,
GeoRS(43), No. 3, March 2005, pp. 441-454.
IEEE Abstract. IEEE Top Reference. 0501 BibRef

Camps-Valls, G., Bruzzone, L.,
Kernel-Based Methods for Hyperspectral Image Classification,
GeoRS(43), No. 6, June 2005, pp. 1351-1362.
IEEE Abstract. IEEE Top Reference. 0506 BibRef

Camps-Valls, G., Bandos Marsheva, T.V., Zhou, D.,
Semi-Supervised Graph-Based Hyperspectral Image Classification,
GeoRS(45), No. 10, October 2007, pp. 3044-3054.
WWW Version. 0711 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

Neher, R., Srivastava, A.,
A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging,
GeoRS(43), No. 6, June 2005, pp. 1363-1374.
IEEE Abstract. IEEE Top Reference. 0506 BibRef

Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., Ramon, H.,
Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps,
RealTimeImg(11), No. 2, April 2005, pp. 75-83.
WWW Version. 0506 BibRef

Tatzer, P.[Petra], Wolf, M.[Markus], Panner, T.[Thomas],
Industrial application for inline material sorting using hyperspectral imaging in the NIR range,
RealTimeImg(11), No. 2, April 2005, pp. 99-107.
WWW Version. 0506 BibRef

Pilevar, A.H., Sukumar, M.,
GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases,
PRL(26), No. 7, 15 May 2005, pp. 999-1010.
WWW Version. 0506 BibRef

Purkis, S.J.,
A 'Reef-Up' Approach to Classifying Coral Habitats From IKONOS Imagery,
GeoRS(43), No. 6, June 2005, pp. 1375-1390.
IEEE Abstract. IEEE Top Reference. 0506Using hyperspectral data, calibrate based on field measurements of reflectance. BibRef

Othman, H., Qian, S.E.,
Noise Reduction of Hyperspectral Imagery Using Hybrid Spatial-Spectral Derivative-Domain Wavelet Shrinkage,
GeoRS(44), No. 2, February 2006, pp. 397-408.
WWW Version. 0602 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.
WWW Version. 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.
WWW Version. 0711 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.
WWW Version. 0711 BibRef

Brown, A.J.,
Spectral Curve Fitting for Automatic Hyperspectral Data Analysis,
GeoRS(44), No. 6, June 2006, pp. 1601-1608.
WWW Version. 0606 BibRef

Weinberger, K.Q.[Kilian Q.], Saul, L.K.[Lawrence K.],
Unsupervised Learning of Image Manifolds by Semidefinite Programming,
IJCV(70), No. 1, October 2006, pp. 77-90.
WWW Version. 0606 BibRef
Earlier: CVPR04(II: 988-995).
IEEE Abstract. IEEE Top Reference. 0408Analyze high dimensional data. BibRef

Renzullo, L.J., Blanchfield, A.L., Powell, K.S.,
A Method of Wavelength Selection and Spectral Discrimination of Hyperspectral Reflectance Spectrometry,
GeoRS(44), No. 7, Part 2, July 2006, pp. 1986-1994.
WWW Version. 0606 BibRef

Kim, J.[Jaehwan], Choi, S.[Seungjin],
Semidefinite spectral clustering,
PR(39), No. 11, November 2006, pp. 2025-2035.
WWW Version. 0608Convex optimization; Multi-way graph equipartitioning; Semidefinite programming; Spectral clustering BibRef

Berge, A.[Asbjørn], Solberg, A.S.[Anne Schistad],
Structured Gaussian Components for Hyperspectral Image Classification,
GeoRS(44), No. 11, November 2006, pp. 3386-3396.
WWW Version. 0611 BibRef

Berge, A.[Asbjrn], Jensen, A.C.[Are C.], Solberg, A.H.S.[Anne H. Schistad],
Sparse Inverse Covariance Estimates for Hyperspectral Image Classification,
GeoRS(45), No. 5, May 2007, pp. 1399-1407.
WWW Version. 0704 BibRef
Earlier: A1, A3, Only:
Sparse Covariance Estimates for High Dimensional Classification Using the Cholesky Decomposition,
SSPR06(835-843).
WWW Version. 0608 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.
WWW Version. 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.
WWW Version. 0703 BibRef

Santurri, L.[Leonardo],
Aliasing assessment in wavelength domain of hyperspectral data,
RealTimeIP(1), No. 2, December 2006, pp. 131-141.
WWW Version. 0001 BibRef

Rud, R.[Ronit], Shoshany, M.[Maxim], Alchanatis, V.[Victor], Cohen, Y.[Yafit],
Application of spectral features' ratios for improving classification in partially calibrated hyperspectral imagery: a case study of separating Mediterranean vegetation species,
RealTimeIP(1), No. 2, December 2006, pp. 143-152.
WWW Version. 0001 BibRef

Kogan, J.[Jacob],
Introduction to Clustering Large and High-Dimensional Data,
Cambridge University Press2006. ISBN-13: 9780521852678
WWW Version. Or:
WWW Version. Focused coverage of a few important algorithms. BibRef 0600

Monteiro, S.T.[Sildomar Takahashi], Minekawa, Y.[Yohei], Kosugi, Y.[Yukio], Akazawa, T.[Tsuneya], Oda, K.[Kunio],
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery,
PandRS(62), No. 1, May 2007, pp. 2-12.
WWW Version. 0709Agriculture; Hyperspectral image; Modeling; Neural networks; Spatial prediction BibRef

Dehaan, R.[Remy], Louis, J.[John], Wilson, A.[Andrea], Hall, A.[Andrew], Rumbachs, R.[Rod],
Discrimination of blackberry (Rubus fruticosus sp. agg.) using hyperspectral imagery in Kosciuszko National Park, NSW, Australia,
PandRS(62), No. 1, May 2007, pp. 13-24.
WWW Version. 0709Hyperspectral imagery; Weeds; Blackberry; Ecosystem management BibRef

Zhao, D.[Dehua], Huang, L.[Liangmei], Li, J.L.[Jian-Long], Qi, J.[Jiaguo],
A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy,
PandRS(62), No. 1, May 2007, pp. 25-33.
WWW Version. 0709Hyperspectral remote sensing; Cotton; Broadband vegetation indices; Narrowband VIs; Leaf area index (LAI); Canopy chlorophyll density (CCD); Bandwidth and wavelength selection BibRef

Hsu, P.H.[Pai-Hui],
Feature extraction of hyperspectral images using wavelet and matching pursuit,
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Galvao, L.S.[Lenio Soares], Formaggio, A.R.[Antonio Roberto], Couto, E.G.[Eduardo Guimaraes], Roberts, D.A.[Dar A.],
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PandRS(63), No. 2, March 2008, pp. 259-271.
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Prasad, S., Bruce, L.M.,
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Renard, N., Bourennane, S., Blanc-Talon, J.,
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Marçal, A.R.S.[André R.S.], Borges, J.S.[Janete S.],
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Zeng, H.[Huiwen], Trussell, H.J.,
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Dimensionality reduction in hyperspectral image classification,
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Sarkar, S., Healey, G.,
Hyperspectral texture classification using generalized Markov fields,
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Yu, S.X., Shi, J.B.[Jian-Bo],
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Gomez Chova, L., Calpe, J., Soria, E., Camps Valls, G., Martin, J.D., Moreno, J.,
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Gu, Y.F.[Yan-Feng], Zhang, Y.[Ye],
Unsupervised subspace linear spectral mixture analysis for hyperspectral images,
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Using feature-vector based analysis, based on principal component analysis and independent component analysis, for analysing hyperspectral images,
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You, H., Chang, E.,
Spin Discriminant Analysis(SDA): Using A One-Dimensional Classifier for High Dimensional Classification Problems,
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Peng, J.[Jing], Heisterkamp, D.R.[Douglas R.], Dai, H.K.,
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Muto, Y., Nagase, H., Hamamoto, Y.,
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A Two-step Fuzzy-bayesian Classification for High Dimensional Data,
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Zhang, J., Zhang, Y., Zou, B., Zhou, T.,
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Zhang, Y.[Ye], Desai, M.D.[Mita D.],
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ICIP98(I: 570-573).
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Bajic, S.C.,
Accuracy of a supervised classification of the artificial objects in thermal hyperspectral images,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Data Anomaly Detection, Hyper-Spectral Anomaly .