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24 October 2007 Clutter characterization within segmented hyperspectral imagery
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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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Steve T. Kacenjar, Michael Hoffberg, and Patrick North "Clutter characterization within segmented hyperspectral imagery", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480D (24 October 2007); https://doi.org/10.1117/12.737084
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