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Proceedings Paper

Change detection in hyperspectral imagery using temporal principal components
Author(s): Vanessa Ortiz-Rivera; Miguel Vélez-Reyes; Badrinath Roysam
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Paper Abstract

Change detection is the process of automatically identifying and analyzing regions that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. Change detection in sequences of hyperspectral images is complicated by the fact that change can occur in the temporal and/or spectral domains. This work studies the use of Temporal Principal Component Analysis (TPCA) for change detection in multi/hyperspectral images. Two additional methods were implemented in order to compare its results with TPCA. These were: Image Differencing and Conventional Principal Component Analysis. Experimental results using phantom hyperspectral imagery taken with Surface Optics SOC-700 hyperspectral camera are presented. The algorithms were implemented using Matlab, and their performance is compared in terms of false alarms, missed changes and overall error. Results show that the performance of TPCA was the best, obtaining the smallest percentages of error, missed changes, and false alarms using global or local threshold. TPCA with local threshold gave the best performance.

Paper Details

Date Published: 8 May 2006
PDF: 10 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623312 (8 May 2006); doi: 10.1117/12.667961
Show Author Affiliations
Vanessa Ortiz-Rivera, Univ. of Puerto Rico/Mayagüez (United States)
Miguel Vélez-Reyes, Univ. of Puerto Rico/Mayagüez (United States)
Badrinath Roysam, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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