Hyperspectral Imaging

[1]  Hsien-Tsai Wu,et al.  Source number estimators using transformed Gerschgorin radii , 1995, IEEE Trans. Signal Process..

[2]  Alfredo Huete,et al.  Separation of soil-plant spectral mixture by factor analysis , 1986 .

[3]  J. Boardman.  Inversion Of Imaging Spectrometry Data Using Singular Value Decomposition , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[4]  Shun-ichi Amari,et al.  Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.

[5]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[6]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[7]  D. Roberts,et al.  A new approach to quantifying abundances of materials in multispectral images , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[8]  Alan D. Stocker,et al.  Real-time hyperspectral detection and cuing , 2000 .

[9]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[10]  John A. Richards,et al.  Efficient maximum likelihood classification for imaging spectrometer data sets , 1994, IEEE Trans. Geosci. Remote. Sens..

[11]  E. Ashton,et al.  Algorithms for the Detection of Su b-Pixel Targets in Multispectral Imagery , 1998 .

[12]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms in Engineering Applications , 1997, Springer Berlin Heidelberg.

[13]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[14]  David A. Landgrebe,et al.  Hyperspectral data analysis and supervised feature reduction via projection pursuit , 1999, IEEE Trans. Geosci. Remote. Sens..

[15]  Chein-I Chang,et al.  A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection , 1997, IEEE Trans. Geosci. Remote. Sens..

[16]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[17]  Joseph W. Boardman,et al.  Inversion of high spectral resolution data , 1990, Other Conferences.

[18]  Fabio Maselli,et al.  Selection of optimum bands from TM scenes through mutual information analysis , 1993 .

[19]  Keinosuke Fukunaga.  15 Intrinsic dimensionality extraction , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[20]  Ronald G. Resmini,et al.  Mineral mapping with HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A. , 1997 .

[21]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[22]  Karen H. Haskell,et al.  An algorithm for linear least squares problems with equality and nonnegativity constraints , 1981, Math. Program..

[23]  J. Boardman,et al.  Leveraging the High Dimensionality of AVIRIS Data for improved Sub-Pixel Target i Unmixing and Rejection of False Positives : Mixture Tuned Matched Filtering , 1998 .

[24]  C.-C. Jay Kuo,et al.  A new initialization technique for generalized Lloyd iteration , 1994, IEEE Signal Processing Letters.

[25]  Chein-I Chang,et al.  Real-time processing algorithms for target detection and classification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[26]  Robin Sibson,et al.  What is projection pursuit , 1987 .

[27]  J. W. Boardman,et al.  Quantitative Determination Of Imaging Spectrometer Specifications Based On Spectral Mixing Models , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[28]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[29]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[30]  B. Hapke.  Bidirectional reflectance spectroscopy: 1. Theory , 1981 .

[31]  Chein-I. Chang,et al.  Discrimination measures for target classification , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[32]  J. G. McWhirter,et al.  A novel algorithm and architecture for adaptive digital beamforming , 1986 .

[33]  Qian Du,et al.  A linear constrained distance-based discriminant analysis for hyperspectral image classification , 2001, Pattern Recognit..

[34]  Chein-I Chang,et al.  An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[35]  John B. Adams,et al.  SPECTRAL MIXTURE ANALYSIS - NEW STRATEGIES FOR THE ANALYSIS OF MULTISPECTRAL DATA , 1994 .

[36]  Mark E. Pesses,et al.  A least-squares-filter vector hybrid approach to hyperspectral subpixel demixing , 1999, IEEE Trans. Geosci. Remote. Sens..

[37]  Ralph Bernstein,et al.  Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[38]  C. O'Connor.  An introduction to multivariate statistical analysis: 2nd edn. by T. W. Anderson. 675 pp. Wiley, New York (1984) , 1987 .

[39]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[40]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[41]  Edmund R. Malinowski,et al.  Theory of error in factor analysis , 1977 .

[42]  Jeff J. Settle,et al.  On the relationship between spectral unmixing and subspace projection , 1996, IEEE Trans. Geosci. Remote. Sens..

[43]  Chein-I Chang,et al.  Further results on relationship between spectral unmixing and subspace projection , 1998, IEEE Trans. Geosci. Remote. Sens..

[44]  Chein-I Chang,et al.  A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks , 2001, IEEE Trans. Geosci. Remote. Sens..

[45]  R. Bro,et al.  A fast non‐negativity‐constrained least squares algorithm , 1997 .

[46]  James B. Farison,et al.  Spatially invariant image sequences , 1992, IEEE Trans. Image Process..

[47]  Chein-I Chang,et al.  An unsupervised vector quantization-based target subspace projection approach to mixed pixel detection and classification in unknown background for remotely sensed imagery , 1999, Pattern Recognit..

[48]  John B. Adams,et al.  Simple Models For Complex Natural Surfaces: A Strategy For The Hyperspectral Era Of Remote Sensing , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[49]  T. Tu.  Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis approach , 2000 .

[50]  David A. Landgrebe,et al.  Hierarchical classifier design in high-dimensional numerous class cases , 1991, IEEE Trans. Geosci. Remote. Sens..

[51]  R. Singer,et al.  Mars - Large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance , 1979 .

[52]  Xiaohui Zhang,et al.  New independent component analysis method using higher order statistics with application to remote sensing images , 2002 .

[53]  Chein-I Chang,et al.  Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images , 2000 .

[54]  S. Amari,et al.  Network Information Criterion | Determining the Number of Hidden Units for an Articial Neural Network Model Network Information Criterion | Determining the Number of Hidden Units for an Articial Neural Network Model , 2007 .

[55]  John W. Tukey,et al.  A Projection Pursuit Algorithm for Exploratory Data Analysis , 1974, IEEE Transactions on Computers.

[56]  J. Boardman.  Automating spectral unmixing of AVIRIS data using convex geometry concepts , 1993 .

[57]  Chein-I Chang,et al.  A computer-aided detection and classification method for concealed targets in hyperspectral imagery , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[58]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[59]  H. Akaike.  A new look at the statistical model identification , 1974 .

[60]  Qian Du,et al.  An interference rejection-based radial basis function neural network for hyperspectral image classification , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[61]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[62]  David A. Landgrebe,et al.  Feature Extraction Based on Decision Boundaries , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[63]  Chein-I. Chang,et al.  An ROC analysis for subpixel detection , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[64]  H. K. Huang,et al.  Evaluation of Diagnostic Systems: Methods from Signal Detection Theory by J. A. Swets and R. M. Pickett , 1983 .

[65]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[66]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[67]  Qian Du,et al.  Noise subspace projection approaches to determination of intrinsic dimensionality of hyperspectral imagery , 1999, Remote Sensing.

[68]  D. C. Heinz,et al.  Fully constrained least-squares based linear unmixing [hyperspectral image classification] , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[69]  Mark A. Girolami,et al.  Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation , 1999 .

[70]  Biing-Hwang Juang,et al.  Discriminative learning for minimum error classification [pattern recognition] , 1992, IEEE Trans. Signal Process..

[71]  Xiaoli Yu,et al.  Comparative performance analysis of adaptive multispectral detectors , 1993, IEEE Trans. Signal Process..

[72]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[73]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[74]  W. Farrand.  Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique , 1997 .

[75]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[77]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[78]  Mark L. G. Althouse,et al.  Least squares subspace projection approach to mixed pixel classification for hyperspectral images , 1998, IEEE Trans. Geosci. Remote. Sens..

[79]  Stephen D. Stearns,et al.  Dimensionality reduction by optimal band selection for pixel classification of hyperspectral imagery , 1993, Optics & Photonics.

[80]  Chein-I Chang,et al.  Chemical vapor detection with a multispectral thermal imager , 1991 .

[81]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[82]  Paul E. Johnson,et al.  A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures , 1983 .

[83]  Tim J. Patterson,et al.  Design of optimal transformations for multispectral change detection using projection pursuit , 1994, Defense, Security, and Sensing.

[84]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[85]  R. E. Roger.  Principal Components transform with simple, automatic noise adjustment , 1996 .

[86]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[87]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[88]  John A. Antoniades,et al.  Use of filter vectors in hyperspectral data analysis , 1995, Optics & Photonics.

[89]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[90]  Chein-I Chang,et al.  Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[91]  Chein-I Chang,et al.  Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[92]  Chein-I Chang,et al.  An oblique subspace projection approach for mixed pixel classification in hyperspectral images , 1999, Pattern Recognit..

[93]  Erkki Oja,et al.  Principal and Independent Components in Neural Networks - Recent Developments , 1995 .

[94]  Chein-I Chang,et al.  Anomaly detection and classification for hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[95]  Chein-I Chang,et al.  A generalized orthogonal subspace projection approach to unsupervised multispectral image classification , 2000, IEEE Trans. Geosci. Remote. Sens..

[96]  Louis L. Scharf,et al.  Signal processing applications of oblique projection operators , 1994, IEEE Trans. Signal Process..

[97]  David A. Landgrebe,et al.  Analyzing high-dimensional multispectral data , 1993, IEEE Trans. Geosci. Remote. Sens..

[98]  Chein-I Chang,et al.  Relationship among orthogonal subspace projection, constrained energy minimization and RX-algorithm , 2002, SPIE Defense + Commercial Sensing.

[99]  Bea Thai,et al.  Invariant subpixel material detection in hyperspectral imagery , 2002, IEEE Trans. Geosci. Remote. Sens..

[100]  Qian Du,et al.  A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[101]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[102]  J. Friedman.  Exploratory Projection Pursuit , 1987 .

[103]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[104]  Hamid Soltanian-Zadeh,et al.  Optimal linear transformation for MRI feature extraction , 1996, IEEE Trans. Medical Imaging.

[105]  Robert J. Schalkoff,et al.  Pattern recognition : statistical, structural and neural approaches / Robert J. Schalkoff , 1992 .

[106]  Chein-I Chang,et al.  Unsupervised interference rejection approach to target detection and classification for hyperspectral imagery , 1998 .

[107]  Qian Du,et al.  Unsupervised target subpixel detection in hyperspectral imagery , 2001, SPIE Defense + Commercial Sensing.

[108]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[109]  Robert F. Cromp,et al.  Analyzing hyperspectral data with independent component analysis , 1998, Other Conferences.

[110]  C W Yang,et al.  Orthogonal subspace projection-based approaches to classification of MR image sequences. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[111]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[112]  J. Boardman,et al.  Geometric mixture analysis of imaging spectrometry data , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[113]  S. Kullback,et al.  Information Theory and Statistics , 1959 .