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Proceedings Paper

Anomalous and matched hyperspectral change detection applications for SHARE data collection
Author(s): Joseph Meola; Jared A. Herweg
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Paper Abstract

Various hyperspectral change detection methods exist in the literature. Here prediction-based methods, such as chronochrome and covariance equalization, are reviewed and compared with a more recently developed model-based approach. These methods are typically applied for anomalous change detection. Several methods for extending these algorithms to achieve matched change detection are discussed. The algorithms are then applied to airborne visible to near infrared hyperspectral data collected recently over Rochester, New York.

Paper Details

Date Published: 15 May 2012
PDF: 11 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839016 (15 May 2012); doi: 10.1117/12.917053
Show Author Affiliations
Joseph Meola, Air Force Research Lab. (United States)
Jared A. Herweg, Rochester Institute of Technology (United States)
Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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