[1] ZHANG B. The advances of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5):1061-1090(in Chinese).
[2] JOSÉ M, ANTONIO P, GUSTAVO C, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36. doi: 10.1109/MGRS.2013.2244672
[3] CHEN H D, PU H H, WANG B, et al. Image euclidean distance-based manifold dimensionality reduction algorithm for hyperspectral imagery[J]. Journal of Infrared and Millimeter Waves, 2013, 32(5):450-451(in Chinese). doi: 10.3724/SP.J.1010.2013.00450
[4] MULLER K R, MIKA S. An introduction in kernel-based learning algorithms[J]. IEEE Transactions on Neural Networks, 2001, 12(2):181-201. doi: 10.1109/72.914517
[5] BACHMANN C M, AINSWORTH T L, FUSINA R A. Exploiting manifold geometry in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3):441-454. doi: 10.1109/TGRS.2004.842292
[6] BACHMANN C M, AINSWORTH T L, FUSINA R A. Improved manifold coordinate representations of large scale hyperspectral scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10):2786-2803. doi: 10.1109/TGRS.2006.881801
[7] ZHANG A, XIE Y. Chaos theory-based data-mining technique for image endmember extraction: Laypunov index and correlation dimension (L and D) [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4):1935-1947. doi: 10.1109/TGRS.2013.2256790
[8] ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326. doi: 10.1126/science.290.5500.2323
[9] TENENBAUM J, SILVA D D, LANGFORD J. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323. doi: 10.1126/science.290.5500.2319
[10] MIKHAIL B, PARTH N. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6):1373-1396. doi: 10.1162/089976603321780317
[11] HE X F, NIYOGI P. Locality preserving projections[J].Neural Information Processing System, 2004, 16:153-160.
[12] DU P J, WANG X M, TAN K, et al. Dimension reduction and feature extraction of hyperspectral remote sensing imagery using manifold learning[J]. Geomatics and Information Science of Wuhan University, 2011, 36(2):148-152(in Chinese).
[13] DING L, TANG P, LI H Y. Dimensionality reduction and classification for hyperspectral remote sensing data using ISOMAP[J]. Infrared and Laser Engineering, 2013, 42(10):2707-2711.
[14] WANG Y T, HUANG S Q, WANG H X, et al. Dimensionality reduction for hyperspectral image based on manifold learning[C]//International Conference on Image and Graphics. New York, USA: Springer International Publishing, 2015: 164-172.
[15] WANG L L, LI Z Y, SUN J X, et al. New measure based manifold algorithm and application in anomaly detection of hyperspectral imagery[J]. Applied Mechanics and Materials, 2011, 80/81(3):797-803.
[16] ZHANG X B, YUAN Y, WANG Q. Spectral discrimination method based on information divergence of gradient[J]. Acta Optica Sinica, 2011, 31(5):0530001(in Chinese). doi: 10.3788/AOS
[17] ZHANG Zh Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM Journal of Scientific Computing, 2004, 26(1):313-338.