An empirical estimate of the multivariate normality of hyperspectral image data

Ariel Schlamm; David Messinger

doi:10.1117/12.881642

20 May 2011 An empirical estimate of the multivariate normality of hyperspectral image data

Ariel Schlamm, David Messinger

Proceedings Volume 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII; 80481J (2011) https://doi.org/10.1117/12.881642
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Historically, much of spectral image analysis revolves around assumptions of multivariate normality. If the background spectral distribution can be assumed to be multivariate normal, then algorithms for anomaly detection, target detection, and classification can be developed around that assumption. However, as the current generation sensors typically have higher spatial and/or spectral resolution, the spectral distribution complexity of the data collected is increasing and these assumptions are no longer adequate, particularly image-wide. However, large portions of the imagery may be accurately described by a multivariate normal distribution. A new empirical method for assessing the multivariate normality of a hyperspectral distribution is presented here. This method assesses the multivariate normality of individual spectral image tiles and is applied to the large area search problem. Additionally, the methodology is applied to a selection of full hyperspectral data sets for general content evaluation. This information can be used to indicate the degree of multivariate normality (or complexity) of the data or data regions and to determine the appropriate algorithm to use globally or locally for spatially adaptive processing.

Citation Download Citation

Ariel Schlamm and David Messinger "An empirical estimate of the multivariate normality of hyperspectral image data", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481J (20 May 2011); https://doi.org/10.1117/12.881642

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