22 October 2001 Complexity of confidence level optimization in sequential automatic target detection of hyperspectral system
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Abstract
This paper focuses on demonstrating the complexity in the optimization process of the sequential algorithm, which is a multi-0stage algorithm with each stage using fewer bands than the previous stages. Specifically, this paper describes the process used to obtain the optimal confidence level and the class separation parameter to quantify the hyperspectral detection performance using the sequential algorithm with Chebyshev's inequality test. This paper also presents the computational complexity involved in reaching the optimum confidence level and the recommended methodology for lessening the computational burden. The detection performance for different spatial resolutions are presented and compared with the ARES baseline performance using all spectral bands. The Forest Radiance I database collected with the HYDICE hyperspectral sensor is utilized. Scenarios include targets in the open, with footprints of 1 m, 2 m and 4 m; and different times of day. The total area coverage and the number of targets used in this evaluation are approximately 10km2 and 108, respectively. The description of the database and sensor parameters can be found.
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Hanna Tran Haskett, Hanna Tran Haskett, Arun K. Sood, Arun K. Sood, Mohammad K. Habib, Mohammad K. Habib, } "Complexity of confidence level optimization in sequential automatic target detection of hyperspectral system", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445387; https://doi.org/10.1117/12.445387
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