Abstract #300786

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JSM 2003 Abstract #300786
Activity Number: 329
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #300786
Title: A Statistical Approach to Subpixel Target Detection
Author(s): Peter Bajorski*+
Companies: Center for Quality and Applied Statistics
Address: 98 Lomb Memorial Dr., Rochester, NY, 14623-5604,
Keywords: hyperspectral image ; subpixel detection ; target space ; background space
Abstract:

In hyperspectral remote sensing, one deals with multidimensional (200+ dimensions) vectors describing reflectance (or radiance) for all pixels in a scene. The subpixel target detectors attempt to find a predefined target (for instance, a type of material), even if it occupies only a fraction of a pixel. Due to atmospheric interference and other sources of error, the target needs to be defined as a whole target subspace rather than a single vector. Another challenge in target detection is the identification of the background space from the scene. The statistical approach investigated in this research attempts to provide statistical characteristics of the target and background spaces, and then use those characteristics in a target detection algorithm.

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