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Proceedings Paper

Clutter characterization within segmented hyperspectral imagery
Author(s): Steve T. Kacenjar; Michael Hoffberg; Patrick North
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Paper Abstract

Use of a Mean Class Propagation Model (MCPM) has been shown to be an effective approach in the expedient propagation of hyperspectral data scenes through the atmosphere. In this approach, real scene data are spatially subdivided into regions of common spectral properties. Each sub-region which we call a class possesses two important attributes (1) the mean spectral radiance and (2) the spectral covariance. The use of this attributes can significantly improve throughput performance of computing systems over conventional pixel-based methods. However, this approach assumes that background clutter can be approximated as having multivariate Gaussian distributions. Under such conditions, covariance propagations can be effectively performed from ground through the atmosphere. This paper explores this basic assumption using real-scene Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and examines how the partitioning of the scene into smaller and smaller segments influences local clutter characterization. It also presents a clutter characterization metric that helps explain the migration of the magnitude of statistical clutter from parent class to child sub-classes populations. It is shown that such a metric can be directly related to an approximate invariant between the parent class and its child classes.

Paper Details

Date Published: 24 October 2007
PDF: 8 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480D (24 October 2007); doi: 10.1117/12.737084
Show Author Affiliations
Steve T. Kacenjar, Lockheed Martin Corp. (United States)
Michael Hoffberg, Lockheed Martin Corp. (United States)
Patrick North, Lockheed Martin Corp. (United States)

Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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