2 August 2002 Overview of algorithms for hyperspectral target detection: theory and practice
Author Affiliations +
Abstract
The purpose of this paper is to provide an overview of the most useful practical algorithms for the detection of targets with known spectral signatures and anomaly detection. First, we provide an overview of adaptive matched filter and anomaly detectors, including their key theoretical assumptions, design parameters, and computational complexity. The emphasis is on the basic ideas that underline the operation of the different algorithms and the geometrical or statistical concepts explaining their performance limitations. Second, we investigate how effectively the signal models used for the development of detection algorithms characterize the HYDICE data. The accurate modeling of the background is crucial for the development of constant false alarm rate (CFAR) detectors. Third, we look at some practical considerations and how they affect the performance of the various algorithms. Finally, we compare the different algorithms with regard to the following two desirable performance properties: capacity to operate in CFAR mode and target visibility enhancement. Since most of these issues are covered more comprehensively in a special issue of IEEE Signal Processing Magazine on Exploiting Hyperspectral Imagery (January 2002), we limit the coverage of this paper to a conceptual framework and a highlight of some experimental results.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dimitris G. Manolakis, "Overview of algorithms for hyperspectral target detection: theory and practice", Proc. SPIE 4725, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, (2 August 2002); doi: 10.1117/12.478752; https://doi.org/10.1117/12.478752
PROCEEDINGS
14 PAGES


SHARE
KEYWORDS
Target detection

Detection and tracking algorithms

Sensors

Expectation maximization algorithms

Algorithm development

Hyperspectral target detection

Statistical analysis

Back to Top