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Proceedings Paper

Methods for multi-temporal change detection
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Paper Abstract

Recent research in hyperspectral change detection has focused on the use of a single reference scene to identify anomalous changes that may be present in the current test scene by suppressing stationary background using various predictive algorithms. This paper extends such algorithms to the use of multiple reference scenes in an attempt to improve change detection performance in hyperspectral images in what is called multi-temporal change detection (MTCD). Often, an airborne hyperspectral sensor performs multiple reconnaissance passes over a specific spatial area. Consequently, a multi-temporal hyperspectral data set may exist for a spatial area of interest and the potential arises to improve clutter suppression and increase change detection performance by using multiple references. This multi-temporal change detection method, of course, requires precise co-registration of all the scenes being used as pixel level changes are being sought. Ground-based hyperspectral data collected using an imaging spectrometer mounted on a pan and tilt is used to perform this study. This method of collection helps ensure precise co-registration between scenes. The scenes are collected over a period of many months and consequently have different illumination and vegetation conditions present. These natural variations between scenes are not considered anomalous changes and should not be targeted by algorithms. A detection scene is collected as well with an anomalous change introduced to allow for testing. Various multi-temporal change detection approaches are derived using single-reference change detection techniques and simple signal processing knowledge. These methods are discussed and compared using ROC analysis on the data available.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340L (27 April 2009); doi: 10.1117/12.816121
Show Author Affiliations
Joseph Meola, Air Force Research Lab. (United States)
Michael T. Eismann, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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