Performance evaluation for hyperspectral target detection algorithms

Xia Sun; Na Li; Hui-jie Zhao

doi:10.1117/12.806467

13 October 2008 Performance evaluation for hyperspectral target detection algorithms

Xia Sun, Na Li, Hui-jie Zhao

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 712725 (2008) https://doi.org/10.1117/12.806467
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

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.

Citation Download Citation

Xia Sun, Na Li, and Hui-jie Zhao "Performance evaluation for hyperspectral target detection algorithms", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 712725 (13 October 2008); https://doi.org/10.1117/12.806467

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