Analyzing hyperspectral data with independent component analysis

Jessica D. Bayliss; J. Anthony Gualtieri; Robert F. Cromp

doi:10.1117/12.300050

1 March 1998 Analyzing hyperspectral data with independent component analysis

Jessica D. Bayliss, J. Anthony Gualtieri, Robert F. Cromp

Proceedings Volume 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors; (1998) https://doi.org/10.1117/12.300050
Event: 26th AIPR Workshop: Exploiting New Image Sources and Sensors, 1997, Washington, DC, United States

Abstract

Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about different materials within a pixel to be obtained. The problem of spectrally unmixing materials may be viewed as a specific case of the blind source separation problem where data consists of mixed signals and the goal is to determine the contribution of each mineral to the mix without prior knowledge of the minerals in the mix. The technique of independent component analysis (ICA) assumes that the spectral components are close to statistically independent and provides an unsupervised method for blind source separation. We introduce contextual ICA in the context of hyperspectral data analysis and apply the method to mineral data from synthetically mixed minerals and real image signatures.

Citation Download Citation

Jessica D. Bayliss, J. Anthony Gualtieri, and Robert F. Cromp "Analyzing hyperspectral data with independent component analysis", Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); https://doi.org/10.1117/12.300050

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