132
4: Stefan A. Robila
Melgani F, Bruzzone L (2002) Support vector machines for classification of hyperspectral
remote-sensing images. International Geoscience and Remote Sensing Symposium,
IGARSS'02, CD
Papoulis A (1991) Probability, random variables, and stochastic processes, McGraw-Hill,
New York
Parra L, Spence CD, Sajda P, Ziehe A, Muller K-R (2000) Unimixing hyperspectral data,
Advances in Neural Information Processing Systems 12: 942-948
Rencher AC (1995) Methods of multivariate analysis. John Wiley and Sons, New York
Richards JA, Jia X (1999) Remote sensing digital image analysis: an introduction. SpringerVerlag, Berlin
Robila SA, Varshney PK (2002) Target detection in hyperspectral images based on independent component analysis. Proceedings of SPIE Automatic Target Recognition XII, 4726,
pp 173-182
Robila SA, Haaland P, Achalakul T, Taylor S (2000) Exploring independent component
analysis for remote sensing. Proceedings of the Workshop on Multi/Hyperspectral Sensors, Measurements, Modeling and Simulation, Redstone Arsenal, Alabama, U.S. Army
Aviation and Missile Command, CD
Tadjudin S, Landgrebe D (1998a) Classification of High Dimensional Data with Limited
Training Samples. Ph.D. thesis, School of Electrical Engineering and Computer Science,
Purdue University.
Tadjudin S, Landgrebe D (1998b) Covariance estimation for limited training samples. International Geoscience and Remote Sensing Symposium (http://dynamo.ecn.purdue.edu/
~ landgreb/LOOCPAMI -web. pdf)
Tu TM, Huang PS, Chen PY (2001) Blind separation of spectral signatures in hyperspectral
imagery. IEEE Proceedings on Vision, Image and Signal Processing 148(4): 217-226
4: Stefan A. Robila
Melgani F, Bruzzone L (2002) Support vector machines for classification of hyperspectral
remote-sensing images. International Geoscience and Remote Sensing Symposium,
IGARSS'02, CD
Papoulis A (1991) Probability, random variables, and stochastic processes, McGraw-Hill,
New York
Parra L, Spence CD, Sajda P, Ziehe A, Muller K-R (2000) Unimixing hyperspectral data,
Advances in Neural Information Processing Systems 12: 942-948
Rencher AC (1995) Methods of multivariate analysis. John Wiley and Sons, New York
Richards JA, Jia X (1999) Remote sensing digital image analysis: an introduction. SpringerVerlag, Berlin
Robila SA, Varshney PK (2002) Target detection in hyperspectral images based on independent component analysis. Proceedings of SPIE Automatic Target Recognition XII, 4726,
pp 173-182
Robila SA, Haaland P, Achalakul T, Taylor S (2000) Exploring independent component
analysis for remote sensing. Proceedings of the Workshop on Multi/Hyperspectral Sensors, Measurements, Modeling and Simulation, Redstone Arsenal, Alabama, U.S. Army
Aviation and Missile Command, CD
Tadjudin S, Landgrebe D (1998a) Classification of High Dimensional Data with Limited
Training Samples. Ph.D. thesis, School of Electrical Engineering and Computer Science,
Purdue University.
Tadjudin S, Landgrebe D (1998b) Covariance estimation for limited training samples. International Geoscience and Remote Sensing Symposium (http://dynamo.ecn.purdue.edu/
~ landgreb/LOOCPAMI -web. pdf)
Tu TM, Huang PS, Chen PY (2001) Blind separation of spectral signatures in hyperspectral
imagery. IEEE Proceedings on Vision, Image and Signal Processing 148(4): 217-226