Robust High-order Matched Filter for Hyperspectral Target Detection with Quasi-Newton Method
Robust high-order matched filter (RHMF),utilizing high-order statistics and considering the inherent variability in target spectral signatures,has obtained better results than other classical detection methods through experiments.However,this algorithm fails to get a fast convergence result by using simple steepest decent.In this paper,we accelerate this algorithm- RHMF successfully by introducing quasi-Newton method and DFP corrector formula,which is a more effective optimization algorithm based on second derivation,into this algorithm.We experiment constrained energy minimization (CEM),adaptive coherence estimator (ACE),RHMF with the steepest descent,and RHMF with quasi-Newton method on real data.The experiment by using RHMF with quasi-Newton has better and faster result,indicating that it is more effective for hyperspectral target detection.We also give the proof of the convergence of this method.
RHMF quasi-Newton hyperspectral target detection
Liu Liu Zhenwei Shi Shuo Yang Haohan Zhang
Image Center, School of Astronautics Beijing University of Aeronautics and Astronautics Beijing, P.R. China, 100191
国际会议
厦门
英文
1-4
2012-12-16（万方平台首次上网日期，不代表论文的发表时间）