红外与毫米波学报, 2017, 36 (4): 471, 网络出版: 2017-10-12  

一种新的用于高光谱图像小目标探测的目标光谱学习算法

AA novel target spectrum learning algorithm for small target detection in hyperspectral imagery
钮宇斌 1,2,*王斌 1,2
作者单位
1 复旦大学 电磁波信息科学教育部重点实验室, 上海 200433
2 复旦大学 信息学院智慧网络与系统研究中心, 上海 200433
引用该论文

钮宇斌, 王斌. 一种新的用于高光谱图像小目标探测的目标光谱学习算法[J]. 红外与毫米波学报, 2017, 36(4): 471.

NIU Yu-Bin, WANG Bin. AA novel target spectrum learning algorithm for small target detection in hyperspectral imagery[J]. Journal of Infrared and Millimeter Waves, 2017, 36(4): 471.

参考文献

[1] Nasrabadi N M. Hyperspectral target detection: an overview of current and future challenges [J]. IEEE Signal Process. Mag., 2014, 31(1): 34-44.

[2] Farrand W H, Harsanyi J C. Mapping the distribution of mine tailings in the Coeur d’Alene River Valley, Idaho, through the use of a constrained energy minimization technique [J]. Remote Sens. Environ., 1997, 59(1): 64–76.

[3] Kraut S, Scharf L L, McWhorter L T. Adaptive subspace detectors [J]. IEEE Trans. Signal Process., 2001, 49(1): 1-16.

[4] Robey F C, Fuhrmann D R, Kelly E J, et al. A CFAR adaptive matched filter detector [J]. IEEE Trans. Aerosp. Electron. Syst., 1992, 28(1): 208-216.

[5] Zou Z X, Shi Z W. Hierarchical suppression method for hyperspectral target detection [J]. IEEE Trans. Geosci. Remote Sens., 2016, 54(1): 330-342.

[6] Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery [J]. IEEE J. Sel. Topics Signal Process., 2011, 5(3): 629-640.

[7] Zhang Y X, Du B, Zhang L P. A sparse representation-based binary hypothesis model for target detection in hyperspectral images [J]. IEEE Trans. Geosci. Remote Sens., 2015, 53(3): 1346-1354.

[8] Zhang L F, Zhang L P, Tao D C, et al. Sparse transfer manifold embedding for hyperspectral target detection, [J]. IEEE Trans. Geosci. Remote Sens., 2014, 52(2): 1030-1043.

[9] Winter M E. N-FINDR: an algorithm for fast autonomous spectral endmember determination in hyperspectral data [C]. In Proc. SPIE, 1999, 3753: 266-275.

[10] Shaw G A, Burke H H. Spectral imaging for remote sensing [J]. Lincoln Lab. J., 2003, 14(1): 3-28.

[11] Wang T, Du B, Zhang L P. An automatic robust iteratively reweighted unstructured detector for hyperspectral imagery [J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7(6): 2367-2382.

[12] Yang S, Shi Z W, Tang W. Robust hyperspectral image target detection using an inequality constraint [J]. IEEE Trans. Geosci. Remote Sens., 2015, 53(6): 3389-3404.

[13] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation [J]. IEEE Trans. Signal Process., 2006, 54(11): 4311-4322.

[14] Zou H, Hastie T, Tibshirani R. Sparse principal component analysis [J]. J. Comp. Graph. Statist., 2006, 15(2): 265-286.

[15] Clark R N, Swayze G A, Gallagher A J, et al. The U.S. geological survey digital spectral library: Version 1: 0.2 to 3.0 μm [C]. U.S. Geol. Surv., Denver, CO, USA, Open File Rep.1993, 93-592.

[16] Mairal J, Bach F, Ponce J, et al. Online learning for matrix factorization and sparse coding [J]. J. Mach. Learn. Res., 2010, vol. 11(1): 19-60.

钮宇斌, 王斌. 一种新的用于高光谱图像小目标探测的目标光谱学习算法[J]. 红外与毫米波学报, 2017, 36(4): 471. NIU Yu-Bin, WANG Bin. AA novel target spectrum learning algorithm for small target detection in hyperspectral imagery[J]. Journal of Infrared and Millimeter Waves, 2017, 36(4): 471.

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