AA novel target spectrum learning algorithm for small target detection in hyperspectral imagery

NIU Yu-Bin; WANG Bin

doi:10.11972/j.issn.1001-9014.2017.04.016

[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.