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5 May 2017 Ensemble learning and model averaging for material identification in hyperspectral imagery
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In this paper we present a method for identifying the material contained in a pixel or region of pixels in a hyperspectral image. An identification process can be performed on a spectrum from an image from pixels that has been pre-determined to be of interest, generally comparing the spectrum from the image to spectra in an identification library. The metric for comparison used in this paper a Bayesian probability for each material. This probability can be computed either from Bayes' theorem applied to normal distributions for each library spectrum or using model averaging.

Using probabilities has the advantage that the probabilities can be summed over spectra for any material class to obtain a class probability. For example, the probability that the spectrum of interest is a fabric is equal to the sum of all probabilities for fabric spectra in the library. We can do the same to determine the probability for a specific type of fabric, or any level of specificity contained in our library. Probabilities not only tell us which material is most likely, the tell us how confident we can be in the material presence; a probability close to 1 indicates near certainty of the presence of a material in the given class, and a probability close to 0.5 indicates that we cannot know if the material is present at the given level of specificity. This is much more informative than a detection score from a target detection algorithm or a label from a classification algorithm.

In this paper we present results in the form of a hierarchical tree with probabilities for each node. We use Forest Radiance imagery with 159 bands.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William F. Basener "Ensemble learning and model averaging for material identification in hyperspectral imagery", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101981I (5 May 2017);

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