高光谱分类体积的端元提取 下载: 737次
[1] 张良培, 张立福. 高光谱遥感[M]. 武汉: 武汉大学出版社, 2005: 23-24.
Zhang L P, Zhang L F. Hyperspectral remote sensing[M].Wuhan: Wuhan University Press, 2005: 23-24.
[2] Plaza A, Martinez P, Perez R, et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 650-663.
[3] Bioucas-Dias J M, Plaza A, Dobigeon N, et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 354-379.
[4] Winter M E. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data[J]. Proceedings of SPIE, 1999, 3753: 266-275.
[5] Nascimento J M P, Dias J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
[6] 王丽姣, 厉小润, 赵辽英. 快速实现基于单形体体积生长的端元提取算法[J]. 光学学报, 2014, 34(11): 1128001.
[7] Boardman J W, Kruse F A, Green R O. Mapping target signatures via partial unmixing of AVIRIS data[C]. Fifth JPL Airborne Earth Science Workshop, 1995: 23-26.
[8] Ambikapathi A M, Chan T H, Ma W K, et al. A robust alternating volume maximization algorithm for endmember extraction in hyperspectral images[C]. Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010: 1-4.
[9] Iordache M D, Bioucas-Dias J M, Plaza A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4484-4502.
[10] 李姝颖, 杜山山, 曾朝阳. 基于特征空间显著性的假目标光谱设计[J]. 光学学报, 2017, 37(1): 0128001.
[11] Geng X, Zhao Y C, Wang F X, et al. A new volume formula for a simplex and its application to endmember extraction for hyperspectral image analysis[J]. International Journal of Remote Sensing, 2010, 31(4): 1027-1035.
[12] Chang C I, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608-619.
[13] Hartigan J A, Wong M A. AlgorithmAS 136: A K-means clustering algorithm[J]. Applied Statistics, 1979, 28(1): 100-108.
[14] Martin G, Plaza A. Region-based spatial preprocessing for endmember extraction and spectral unmixing[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 745-749.
[15] Miao L D, Qi H R. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765-777.
[16] Heinz D C, Chang C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529-545.
严阳, 华文深, 崔子浩, 伍锡山, 刘恂. 高光谱分类体积的端元提取[J]. 激光与光电子学进展, 2018, 55(9): 093004. Yan Yang, Hua Wenshen, Cui Zihao, Wu Xishan, Liu Xun. Classification and Volume for Hyperspectral Endmember Extraction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 093004.