References
131
provides a stronger separation than simple decorrelation and allows a better
processing for further class identification (either classification or target detection). The ICA can be viewed, therefore, as an improvement over PCA as
an unsupervised feature extraction tool. A more extensive discussion on ICA
based feature extraction is presented in Chap. 8.
We also note that apart from the direct application of ICA to hyperspectral
imagery, other approaches have been used. A very promising new method,
associating mixtures of independent components to the classes, and thus allowing unsupervised classification is presented in Chap. 9.
References
Back AD, Weingend AS (1997) A first application of independent component analysis in
evaluating structure from stock returns. International Journal of Neural Systems 8(4):
473-484
Bayliss J, Gualtieri JA, Cromp RF (1997) Analyzing hyperspectral data with independent
component analysis. Proceedings of SPIE 26th AIPR Workshop: Exploiting New Image
Sources and Sensors, 3240, pp 133-143
Bell A, Sejnowski TJ (1995) An information-maximization approach to blind separation
and blind deconvolution. Neural Computation 7: 1129-1159
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.
Chen CH, Zhang X (1999) Independent component analysis for remote sensing. Proceedings
ofSPIE Image and Signal Processing Conference for Remote Sensing V, 3871, pp 150-157
Chiang S-S, Chang C-I, Ginsberg IW (2001) Unsupervised target detection in hyperspectral
images using projection pursuit. IEEE Transactions on Geoscience and Remote Sensing
39(7): 1380-1391.
Cichocki A, Amari S-I (2002) Adaptive blind signal and image processing -learning algorithms and applications. John Wiley and Sons, New York
Common P (1994) Independent component analysis, a new concept? Signal Processing 36:
287-314.
Girolami M (2000) Advances in independent component analysis. Springer, London, New
York
Gualtieri JA, Cromp RF (1998) Support vector machines for hyperspectral remote sensing
classification. Proceeding of the SPIE, 27th AIPR Workshop, Advances in Computer
Assisted Recognition, 3584, pp 221-232
Gualtieri JA, Chettri SR, Cromp RE, Johnson LF (1999). Support vector machine classifiers as
applied to AVIRIS data. Summaries of the Eighth JPL Airborne Earth Science Workshop
Haykin S (Ed) (2000) Unsupervised adaptive filtering: blind source separation 1. John Wiley
and Sons, New York
Healey G, Kuan C (2002) Using source separation methods for end-member selection.
Proceedings of SPIE Aerosense, 4725, pp 10-17
Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. John Wiley and
Sons, New York
Lee TW (1998) Independent component analysis: theory and applications. Kluwer Academic
Publishers, Boston
131
provides a stronger separation than simple decorrelation and allows a better
processing for further class identification (either classification or target detection). The ICA can be viewed, therefore, as an improvement over PCA as
an unsupervised feature extraction tool. A more extensive discussion on ICA
based feature extraction is presented in Chap. 8.
We also note that apart from the direct application of ICA to hyperspectral
imagery, other approaches have been used. A very promising new method,
associating mixtures of independent components to the classes, and thus allowing unsupervised classification is presented in Chap. 9.
References
Back AD, Weingend AS (1997) A first application of independent component analysis in
evaluating structure from stock returns. International Journal of Neural Systems 8(4):
473-484
Bayliss J, Gualtieri JA, Cromp RF (1997) Analyzing hyperspectral data with independent
component analysis. Proceedings of SPIE 26th AIPR Workshop: Exploiting New Image
Sources and Sensors, 3240, pp 133-143
Bell A, Sejnowski TJ (1995) An information-maximization approach to blind separation
and blind deconvolution. Neural Computation 7: 1129-1159
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.
Chen CH, Zhang X (1999) Independent component analysis for remote sensing. Proceedings
ofSPIE Image and Signal Processing Conference for Remote Sensing V, 3871, pp 150-157
Chiang S-S, Chang C-I, Ginsberg IW (2001) Unsupervised target detection in hyperspectral
images using projection pursuit. IEEE Transactions on Geoscience and Remote Sensing
39(7): 1380-1391.
Cichocki A, Amari S-I (2002) Adaptive blind signal and image processing -learning algorithms and applications. John Wiley and Sons, New York
Common P (1994) Independent component analysis, a new concept? Signal Processing 36:
287-314.
Girolami M (2000) Advances in independent component analysis. Springer, London, New
York
Gualtieri JA, Cromp RF (1998) Support vector machines for hyperspectral remote sensing
classification. Proceeding of the SPIE, 27th AIPR Workshop, Advances in Computer
Assisted Recognition, 3584, pp 221-232
Gualtieri JA, Chettri SR, Cromp RE, Johnson LF (1999). Support vector machine classifiers as
applied to AVIRIS data. Summaries of the Eighth JPL Airborne Earth Science Workshop
Haykin S (Ed) (2000) Unsupervised adaptive filtering: blind source separation 1. John Wiley
and Sons, New York
Healey G, Kuan C (2002) Using source separation methods for end-member selection.
Proceedings of SPIE Aerosense, 4725, pp 10-17
Hyvarinen A, Karhunen J, Oja E (2001) Independent component analysis. John Wiley and
Sons, New York
Lee TW (1998) Independent component analysis: theory and applications. Kluwer Academic
Publishers, Boston