A signal processing perspective of hyperspectral imagery analysis techniques
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Authors
Stefanou, Marcus Stavros.
Subjects
NA
Advisors
Olsen, Richard Christopher
Cristi, Roberto
Date of Issue
1997-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
A new class of remote sensing data with great potential for the accurate identification of surface materials is termed hyperspectral imagery. Airborne or satellite imaging spectrometers record reflected solar or emissive thermal electromagnetic energy in hundreds of contiguous narrow spectral bands. The substantial dimensionality and unique character of hyperspectral imagery require techniques which differ substantially from traditional imagery analysis. One such approach is offered by a signal processing 'paradigm, which seeks to detect signals in the presence of noise and multiple interfering signals. This study reviews existing hyperspectral imagery analysis techniques from a signal processing perspective and arranges them in a contextual hierarchy. It focuses on a large subset of analysis techniques based on linear transform and subspace projection theory, a well established part of signal processing. Four broad families of linear transformation-based analysis techniques are specified by the amounts of available a priori scene information. Strengths and weaknesses of each technique are developed. In general, the spectral angle mapper (SAM) and the orthogonal subspace projection (OSP) techniques gave the best results and highest signal-to-clutter ratios (SCRs). In the case of minority targets, where a small number of target pixels occurred over the entire scene, the low probability of detection (LPD) technique performed well.
Type
Thesis
Description
Series/Report No
Department
Electrical Engineering
Organization
Identifiers
NPS Report Number
Sponsors
Funder
NA
Format
xix, 218 p.;28 cm.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.