<title>Statistics of hyperspectral imaging data</title>

Abstract

Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral imaging (HSI) data is crucial for several applications, including the design of constant false alarm rate (CFAR) detectors and statistical classifiers. HSI data are vector (or equivalently multivariate) data in a vector space with dimension equal to the number of spectral bands. As a result, the scalar statistics utilized by many detection and classification algorithms depend upon the joint pdf of the data and the vector-to--scalar mapping defining the specific algorithm. For reasons of analytical tractability, the multivariate Gaussian assumption has dominated the development and evaluation of algorithms for detection and classification in HSI data, although it is widely recognized that it does not always provide an accurate model for the data. The purpose ofthis paper is to provide a detailed investigation ofthejoint and marginal distributional properties of HSI data. To this end, we assess how well the multivanate Gaussian pdf describes HSI data using univariate techniques for evaluating marginal normality, and techniques that use unidimensional views (projections) of multivanate data. We show that the class of elliptically contoured distributions, which includes the multivariate normal distribution as a special case, provides a better characterization of the data. Finally, it is demonstrated that the class of univariate stable random variables provides a better model for the heavy-tailed output distribution of the well known matched filter target detection algorithm.

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

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Overview of Physical Models and Statistical Approaches for Weak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery

“…Several studies have investigated the distribution of background clutter [7,15,[18][19][20][21][22][23][24][25] in IR scenes. There is complete agreement that the distribution of is not accurately described as a SCMG.…”

Uses of WorldView-2 Multispectral Data in Extracting Potential Iron Mineralization Zones

“…SMF 'filters' the imput image for good matches to the chosen target spectrum by maximizing the response of the target spectrum within the data and suppressing the response of everything else. Applications of SMF include planetary mapping [5], plant species mapping [6], vegetation mapping [7][8][9], landform mapping [10][11][12], weak gas plumes detection [13], etc.…”