| Citation: |
WU Xiangwei, GUO Baofeng, CHEN Chunzhong, SHEN Honghai. Anomaly detection based weighted combination kernel RX algorithm and its parameter selection[J]. LASER TECHNOLOGY, 2015, 39(6): 745-750. DOI: 10.7510/jgjs.issn.1001-3806.2015.06.003
|
| [1] |
MATTEOLI S, DIANI M, CORSINI G. A tutorial overview of anomaly detection in hyperspectral images [J]. IEEE Transactions on Aerospace and Electronic Systems Magazine, 2010, 25(7): 5-28.
|
| [2] |
ECHES O, DOBIGEON N, TOURNERET J Y. Enhancing hyperspectral image unmixing with spatial correlations [J]. IEEE Transactions on Geosciences and Remote Sensing, 2011, 49(11): 4239-4247.
|
| [3] |
KWON H, DER S Z, NASRABADI N M. Adaptive anomaly detection using subspace separation for hyperspectral imagery [J]. Optical Engineering, 2003, 42(11): 3342-3351.
|
| [4] |
REED I S, YU X L. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution [J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1990, 38(10): 1760-1770.
|
| [5] |
KWON H, NASRABADI N M. Kernel RX algorithm a nonlinear anomaly detector for hyperspectral imagery[J]. IEEE Transactions on Geosciences and Remote Sensing, 2005, 43(2): 388-397.
|
| [6] |
WANG L P, ZHANG L P, HAN J T. Detecting algorithm of moving target in dynamic background based on gray-weighted kernel function [J]. Infrared and Laser Engineering, 2013, 42(12): 3453-3457 (in Chinese).
|
| [7] |
CHEN D R, SUN B, TAO P, et al. Spatial neighboring clustering method for hyperspectral imagery based on kernel spectral angel cosine [J]. Acta Electronica Sinica, 2008, 36(10): 1992-1995(in Chinese).
|
| [8] |
HAN J, YUE J, ZHANG Y, et al. SAM weighted KEST algorithm for anomaly detection in hyperspectral imagery [J]. Journal of Infrared and Millimeter Waves, 2013, 32(4): 359-365(in Chinese).
|
| [9] |
WANG S B. Study on anomaly target detection technology in hyperspectral images [D]. Haerbin: Harbin Institute of Technology, 2010: 22-35(in Chinese).
|
| [10] |
WU X M. The research on hyperspectral imagery unmixing technology based on kernel methods [D]. Hangzhou:Zhejiang University, 2011: 15-33(in Chinese).
|
| [11] |
WANG Ch. Research on multiple kernel learning based target interpretation technologies in hyperspectral imagery [D].Haerbin: Harbin Institute of Technology, 2011: 13-23(in Chinese).
|
| [12] |
GAN P P, WANG R Sh. Spectral remote sensing recognition basis and technology research [J]. Remote Sensing Technology and Application, 2002, 17(3): 53-60(in Chinese).
|
| [13] |
SHAWE-TAYLOR J, CRISTIANINI N. Pattern recognition of kernel method [M]. 2nd ed. Beijing: China Machine Press, 2006: 29-50(in Chinese).
|
| [14] |
DANG J W, WANG Y P, DI F W, et al. Artificial intelligence [M]. Beijing: Electronic Industry Press, 2012: 178-180(in Chinese).
|
| [15] |
EBERHART R, KENNEDY J. A new optimizer using particle swarm theory[C]//Proceedings of the 6th International Symposium on Micro Machine and Human Science. New York, USA : IEEE , 1995: 39-43.
|
| [16] |
GAO X J. On classification of hyperspectral remotely sensed imagery based on support vector machines[D]. Hangzhou: Hangzhou Dianzi University, 2012: 42-44 (in Chinese) .
|
| [17] |
LIANG Zh J, WANG K F, GU G Q, et al . Digital speckle image correlation method base on particle swarm optimization algorithm[J]. Laser Technology, 2014, 38(5): 603-607 (in Chinese).
|
| 1. |
秦树旺，毛耀，包启亮. 光电跟踪系统的模糊Ⅱ型控制技术研究. 激光技术. 2021(02): 147-154 .
 本站查看
| |
| 2. |
郑羽，李东，王艳林. 基于FPGA的精密指向组件控制系统设计. 工业控制计算机. 2019(07): 12-14+17 .
|
DownLoad: