On the use of ICA for hyperspectral image analysis

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyper-spectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.

[1]  B. Achiriloaie,et al.  VI REFERENCES , 1961 .

[2]  P. Comon Independent Component Analysis , 1992 .

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

[4]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[5]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .

[6]  Andreas Ziehe,et al.  Unmixing Hyperspectral Data , 1999, NIPS.

[7]  M. Lennon,et al.  Independent component analysis as a tool for the dimensionality reduction and the representation of hyperspectral images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[8]  Francis Eng Hock Tay,et al.  A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines , 2001, Intell. Data Anal..

[9]  José M. Bioucas-Dias,et al.  Does independent component analysis play a role in unmixing hyperspectral data? , 2005, IEEE Trans. Geosci. Remote. Sens..

[10]  Jing Wang,et al.  Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Jon Atli Benediktsson,et al.  On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation , 2008, Neurocomputing.

[12]  Jon Atli Benediktsson,et al.  Gradient Optimization for multiple kernel's parameters in support vector machines classification , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Jon Atli Benediktsson,et al.  Independent Component Discriminant Analysis for hyperspectral image classification , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.