References
235
For the AVIRIS dataset preprocessed by each of the four feature extraction
techniques, the ICAMM classification algorithm produced significantly higher
accuracy as compared to the K-means algorithm. Similar results have been
obtained for the data acquired by another hyperspectral sensor - HYDICE and
have been reported in Shah (2003).
References
Bae U-M, Lee T-W, Lee S-Y (2000) Blind signal separation in teleconferencing using ICA
mixture model. Electronic Letters 36: 680-682
Chang C-I, Chiang S-S, Smith JA, Ginsberg IW (2002) Linear spectral random mixture analysis for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing
40(2): 375-392.
Common P (1994) Independent component analysis, a new concept? Signal Processing 36:
287-314.
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edition. John Wiley and Sons,
New York
Harsanyi JC, Chang C-I (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Transactions on Geoscience
and Remote Sensing 32: 779-785
Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Transactions
on Information Theory 14: 55-63
Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications.
Neural Networks 13: 411-430
Ifarraguerri A, Chang C-I (2000) Unsupervised hyperspectral image analysis with projection
pursuit. IEEE Transactions on Geoscience and Remote Sensing 38: 2529-2538.
Landgrebe 0 (2002) Hyperspectral image data analysis. IEEE Signal Processing Magazine
19: 17-28
Lee T-W, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural
Computation 11: 417-441
Lee T-W, Lewicki MS, Sejnowski TJ (2000) ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation.
IEEE Transactions on Pattern Analysis and Machine Intelligence 22: 1078-1089
Lee T -W, Lewicki MS (2002) Unsupervised image classification, segmentation, and enhancement using ICA mixture models. IEEE Transactions on Image Processing 11: 270-279
Papoulis A (1991) Probability, random variables, and stochastic processes, 3rd edition.
McGraw Hill, New York
Richards JA, Jia X (1999a) Remote sensing digital image analysis: an introduction. SpringerVerlag, Berlin
Richards JA, Jia X (1999b) Segmented principal components transformation for efficient
hyperspectral remote sensing image display and classification. IEEE Transactions on
Geoscience and Remote Sensing 37: 538-542
Robila SA, (2002) Independent component analysis feature extraction for hyperspectral
images, PhD Thesis, Syracuse University, Syracuse, NY
Robila SA, Varshney PK (2002a) Target detection in hyperspectral images based on independent component analysis. Proceedings of SPIE Automatic Target Recognition XII,
4726, pp 173-182
235
For the AVIRIS dataset preprocessed by each of the four feature extraction
techniques, the ICAMM classification algorithm produced significantly higher
accuracy as compared to the K-means algorithm. Similar results have been
obtained for the data acquired by another hyperspectral sensor - HYDICE and
have been reported in Shah (2003).
References
Bae U-M, Lee T-W, Lee S-Y (2000) Blind signal separation in teleconferencing using ICA
mixture model. Electronic Letters 36: 680-682
Chang C-I, Chiang S-S, Smith JA, Ginsberg IW (2002) Linear spectral random mixture analysis for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing
40(2): 375-392.
Common P (1994) Independent component analysis, a new concept? Signal Processing 36:
287-314.
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edition. John Wiley and Sons,
New York
Harsanyi JC, Chang C-I (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Transactions on Geoscience
and Remote Sensing 32: 779-785
Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Transactions
on Information Theory 14: 55-63
Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications.
Neural Networks 13: 411-430
Ifarraguerri A, Chang C-I (2000) Unsupervised hyperspectral image analysis with projection
pursuit. IEEE Transactions on Geoscience and Remote Sensing 38: 2529-2538.
Landgrebe 0 (2002) Hyperspectral image data analysis. IEEE Signal Processing Magazine
19: 17-28
Lee T-W, Girolami M, Sejnowski TJ (1999) Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources. Neural
Computation 11: 417-441
Lee T-W, Lewicki MS, Sejnowski TJ (2000) ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation.
IEEE Transactions on Pattern Analysis and Machine Intelligence 22: 1078-1089
Lee T -W, Lewicki MS (2002) Unsupervised image classification, segmentation, and enhancement using ICA mixture models. IEEE Transactions on Image Processing 11: 270-279
Papoulis A (1991) Probability, random variables, and stochastic processes, 3rd edition.
McGraw Hill, New York
Richards JA, Jia X (1999a) Remote sensing digital image analysis: an introduction. SpringerVerlag, Berlin
Richards JA, Jia X (1999b) Segmented principal components transformation for efficient
hyperspectral remote sensing image display and classification. IEEE Transactions on
Geoscience and Remote Sensing 37: 538-542
Robila SA, (2002) Independent component analysis feature extraction for hyperspectral
images, PhD Thesis, Syracuse University, Syracuse, NY
Robila SA, Varshney PK (2002a) Target detection in hyperspectral images based on independent component analysis. Proceedings of SPIE Automatic Target Recognition XII,
4726, pp 173-182