Automatic target recognition for hyperspectral imagery using high-order statistics

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Automatic_target_recognition_for_hyperspectral_imagery_using_highorder_statistics.pdf (2.94 MB)

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https://ieeexplore.ieee.org/document/4107995

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https://doi.org/10.1109/TAES.2006.314578
 http://hdl.handle.net/11603/34328

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UMBC Faculty Collection
UMBC Computer Science and Electrical Engineering Department
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Author/Creator ORCID

https://orcid.org/0000-0002-5450-4891

Date

2006-10

Type of Work

journal articles
Text

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Program

Citation of Original Publication

Ren, Hsuan, Qian Du, Jing Wang, Chein-I Chang, James O. Jensen, and Janet L. Jensen. “Automatic Target Recognition for Hyperspectral Imagery Using High-Order Statistics.” IEEE Transactions on Aerospace and Electronic Systems 42, no. 4 (October 2006): 1372–85. https://doi.org/10.1109/TAES.2006.314578.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain

Subjects

Hyperspectral imaging
Hyperspectral sensors
Image analysis
Image resolution
Image sensors
Object detection
Signal processing
Signal resolution
Statistics
Target recognition

Abstract

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective.