[1] DU B, ZHANG Y X, ZHANG L P, et al. A hypothesis in dependent subpixel target detector for hyperspectral images[J]. Signal Process, 2015, 110:244-249. doi: 10.1016/j.sigpro.2014.08.018
[2] WANG Q, YANG G, ZHANG J F, et al. Unsupervised band selection algorithm combined with K-L divergence and mutual information [J]. Laser Technology, 2018, 42(3):417-721 (in Chinese).
[3] DALM M, BUXTON M W N, RUITENBEEK F J A. Discriminating ore and waste in a porphyry copper deposit using short-wavelength infrared (SWIR) hyperspectral imagery[J]. Minerals Engineering, 2017, 105:10-18. doi: 10.1016/j.mineng.2016.12.013
[4] KATHRYN E W, SVEIN K S, MARTIN H S, et al. Non-invasive assessment of packaged cod freeze-thaw history by hyperspectral imaging[J]. Journal of Food Engineering, 2017, 205: 64-73. doi: 10.1016/j.jfoodeng.2017.02.025
[5] LUO Sh Zh, WANG Ch, XI X H, et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation[J]. Ecological Indicators, 2017, 73:378-387. doi: 10.1016/j.ecolind.2016.10.001
[6] XIA J Sh, FALCO N, BENEDIKTSSON J A, et al. Hyperspectral image classification with rotation random forest via KPCA[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4):1601-1609. doi: 10.1109/JSTARS.2016.2636877
[7] WANG Y, GUO L, LIANG N. A dimensionality reduction method based on KPCA with optimized sample set for hyperspectral image[J]. Acta Photonica Sinica, 2011, 40(6): 847-851(in Chinese). doi: 10.3788/gzxb
[8] CHEONG H P, HAESUN P.A comparison of generalized linear discriminant analysis algorithms [J].Pattern Recognition, 2008, 41(3): 1083-1097. doi: 10.1016/j.patcog.2007.07.022
[9] SMARAJIT B, AMITA P, RITA S, et al. Generalized quadratic discriminant analysis [J]. Pattern Recognition, 2015, 48(8): 2676-2684. doi: 10.1016/j.patcog.2015.02.016
[10] XIANG Y J, YANG G, ZHANG J F, et al. Dimensionality reduction for hyperspectral imagery manifold learning based on spectral gradient angles [J]. Laser Technology, 2017, 41(6): 921-926(in Ch-inese).
[11] GU Y F, CHEN W, DI Y, et al. Representative multiple kernel learning for classification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7): 2852-2865 doi: 10.1109/TGRS.2011.2176341
[12] ZHAI Y G, ZHANG L F, WANG N, et al. A modified locality-preserving projection approach for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(8):1059-1063. doi: 10.1109/LGRS.2016.2564993
[13] HE F, WANG R, YU Q, et al. Feature extraction of hyperspectral image of weighted spatial and spectral locality preserving projectio[J]. Optics and Precision Engineering, 2017, 25(1):263-273(in Chinese). doi: 10.3788/OPE.
[14] WRIGHT J, YANG A Y, GANESH A. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(2): 210-227.
[15] HE Zh, LIU L, ZHOU S H, et al. Learning group-based sparse and low-rank representation for hyperspectral image classification[J]. Pattern Recognition, 2016, 60:1041-1056. doi: 10.1016/j.patcog.2016.04.009
[16] ZHANG E L, ZHANG X R, JIAO L Ch, et al. Weighted multifeature hyperspectral image classification via kernel joint sparse representation [J]. Neurocomputing, 2016, 178:71-86. doi: 10.1016/j.neucom.2015.07.114
[17] ZHANG L, YANG M, FENG X. Sparse representation or collaborative representation: Which helps face recognition?[C]//Proceedings of IEEE International Conference on Computer Vision(ICCV). New York, USA: IEEE, 2011: 471-478.
[18] LI J Y, ZHANG H Y, HUANG Y Ch, et al. Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3707-3719. doi: 10.1109/TGRS.2013.2274875
[19] LI W, DU Q. Joint within-class collaborative representation for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2200-2208. doi: 10.1109/JSTARS.2014.2306956
[20] YUAN M D, FENG D Zh, LIU W J, et al. Collaborative representation discriminant embedding for image classification[J]. Journal of Visual Communication and Image Representation, 2016, 41:212-224. doi: 10.1016/j.jvcir.2016.10.001
[21] ZHANG G Q, SUN H J, XIA G Y, et al. Kernel collaborative re-presentation based dictionary learning and discriminative projection [J]. Neurocomputing, 2016, 207:300-309. doi: 10.1016/j.neucom.2016.04.044
[22] CAI S J, ZHANG L, ZUO W M, et al. A probabilistic collaborative representation based approach for pattern classification[C]//IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 2950-2959.
[23] LI L, GE H W, GAO J Q. A spectral-spatial kernel-based method for hyperspectral imagery classification [J]. Advances in Space Research, 2017, 59(4): 954-967. doi: 10.1016/j.asr.2016.11.006
[24] HOU B H, YAO M L, WANG R, et al. Spatial-spectral semi-supervised local discriminant analysis for hyperspectral image classification[J]. Acta Optica Sinaca, 2017, 37(7):0728002(in Chinese). doi: 10.3788/AOS