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Proceedings Paper

Performance evaluation for hyperspectral target detection algorithms
Author(s): Xia Sun; Na Li; Hui-jie Zhao
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Paper Abstract

The quantitative evaluation of detection algorithms performance is a key for the advancement of target detection algorithms. The receiver operator Characteristic (ROC) curve method is purposed to evaluate the detection algorithms performance for hyperspectral data in the basis of the analysis and comparison of kinds of evaluation methods. A ROC curve plots the probability of detection (PD) versus the probability of false alarm (PFA) as a function of the threshold, and the detection performance can be synthetically evaluated using the shape of ROC curve and the area under the curve. The algorithm and modeling method are presented in our work. The ROC curve is applied to evaluate the performance of independent component analysis (ICA), RX, gauss markov random field (GMRF), and projection pursuit (PP) algorithms for hyperspectral remote sensing data.

Paper Details

Date Published: 13 October 2008
PDF: 6 pages
Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 712725 (13 October 2008); doi: 10.1117/12.806467
Show Author Affiliations
Xia Sun, Beihang Univ. (China)
Na Li, Beihang Univ. (China)
Hui-jie Zhao, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 7127:
Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence

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