遥感技术与应用 2019, Vol. 34 Issue (5): 1028-1039 DOI: 10.11873/j.issn.1004-0323.2019.5.1028 |
遥感应用 |
|
|
|
|
凸体几何光谱解混研究进展及若干问题浅析 |
许宁1,2( ),胡玉新1,2,3,耿修瑞1,2,3 |
1. 中国科学院空间信息处理与应用系统技术重点实验室,北京 100190 2. 中国科学院电子学研究所,北京 100190 3. 中国科学院大学,北京 100049 |
|
A Review and Brief Analysis of Convex Geometry-based Spectral Unmixing Methods for Hyperspectal Imagery |
Ning Xu1,2( ),Yuxin Hu1,2,3,Xiurui Geng1,2,3 |
1. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, IECAS, Beijing 100190, China 2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China 3. University of Chinese Academy of Sciences, Beijing 100049, China |
1 |
Tong Qingxi, Zhang Bing, Zheng Lanfen. Hyperspectral Reomte Sensing-Principle,Technology and Applicaton[M]. Beijing:Higher Education Press, 2006.童庆禧,张兵,郑兰芬.高光谱遥感—原理、技术与应用[M].北京:高等教育出版社,2006.
|
2 |
Bioucas-Dias J M, Plaza A, Dobigeon N. 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.
|
3 |
Settle J J, Drake N A. Linear Mixing and The Estimation of Ground Cover Proportions [J]. International Journal of Remote Sensing, 1993, 14(6): 1159-1177.
|
4 |
Tand Wei, Shi Z W, Wu Y, et al. Sparse Unmixing of Hyperspectral Data Using Spectral a Priori Information [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 770-783.
|
5 |
Zadeh M H, Tangestani M H, Roldan F V, et al. Sub-pixel Mineral Mapping of a Porphyry Copper Belt Using EO-1 Hyperion Data [J]. Advances in Space Research, 2014, 53(3): 440-451.
|
6 |
Zhang C Y. Multiscale Quantification of Urban Composition from EO-1/Hyperion Data Using Object-based Spectral Unmixing [J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 47(1):153-162.
|
7 |
Yang C H, Everitt J H, Du Q. Applying Linear Spectral Unmixing to Airborne Hyperspectral Imagery for Mapping Yield Variability in Grain Sorghum and Cotton Fields [J]. Journal of Applied Remote Sensing, 2010, 4(1): 1-11.
|
8 |
Keshava N, Mustard J F. Spectral Unmixing [J]. IEEE Signal Processing Magzine, 2002, 19(1): 44-57.
|
9 |
Xu Ning. Study on Hyperspectral Remote Sensing Imagery Unmixing based on Spectral and Spatial Information[D]. Beijing: University of the Chinese Academy of Sciences, 2016.许宁.基于光谱和空间信息的高光谱图像解混方法研究[D].北京:中国科学院大学, 2016.
|
10 |
Wang Maozhi, Xu Wenxi, Wang Lu, et al. Research Progess on Endmember Extraciton Algorithm and Its Classificaton of Hypersepctral Remote Sensing Imagery[J]. Remote Sensing Technology and Application, 2015,30(4):616-625.王茂芝,徐文皙,王璐,等.高光谱遥感影像端元提取算法研究进展及分类[J].遥感技术与应用,2015, 30(4): 615-625.
|
11 |
Lan Jinhui,Zou Jinlin,Hao Yanshuang,et al.Research Progress on Unmiging of Hyperspectral Remote Sensing Imagery[J].Journal of Remote Sensing,2018,22(1):13-27.蓝金辉,邹金霖,郝彦爽,等.高光谱遥感影像混合像元分解研究进展[J].遥感学报,2018,22(1):13-27.
|
12 |
Liu Ailin,Guo Baoping,Li Yanshan.Piecewise Convex Multiple-model Hyperspectral Imagery Endmember-extraction based on Discrete Particle Swarm Optimaization[J].Remote Sensing Technology and Application,2018,33(2):227-232.刘爱林,郭宝平,李岩山.基于离散粒子群算法的凸多模态高光谱图像端元提取研究[J].遥感技术与应用,2018,33(2):227-232.
|
13 |
Chang C I, Chen S Y, Li H C, et al. Comparative Study and Analysis among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4280-4306.
|
14 |
Adams J B, Smith M O, Gillspie A.R. Simple Models for Complex Natural Surfaces: A Strategy for The Hyperspectral Era of Remote Sensing[C]//IEEE International Geoscience and Remote Sensing Symposium, British Columbia, Canada, 1989.
|
15 |
Craig M D. Unsupervised Unmixing of Remotely Sensed Images[C]//Proceedings of the Fifth Australasian Remote Sensing Conference, Perth, Australia, 1990.
|
16 |
Boardman J W. Automating Spectral Unmixing of AVIRIS Data Using Convex Geometry Concepts [C]//Proceedings of the JPL Airborne Earth Science Workshop, California,1993.
|
17 |
Boardman J W, Kruse F A, Green R O. Mapping Target Signatures via Partial Unmixing of AVIRIS Data[C]//in Proceedings of the JPL Airborne Earth Science Workshop, Pasadena, CA, 1995.
|
18 |
Chang C I, Plaza A. A Fast Iterative Algorithm for Implementation of Pixel Purity Index [J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 63-67.
|
19 |
Chang C I, Wu C C, Chen H M. Random Pixel Purity Index Algorithm [J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(2): 324-328.
|
20 |
Chang C I, Wu C C. Design and Development of Iterative Pixel Purity Index [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2676-2695.
|
21 |
Cui Jiantao,Wang Jing,Li Xiaorun,et al.Endmember Extraction Algorithm based on Spatial Pixel Purity Index[J]. Journal of Zhejiang University(Engineering Science Edition), 2013,47(9):1524-1530.崔建涛,王晶,厉小润,等.基于空间像素纯度指数的端元提取算法[J].浙江大学学报(工学版), 2013, 47(9): 1524-1530.
|
22 |
Nascimento J M, Bioucas-Dias J M. Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
|
23 |
Ren H, Chang C I. Automatic Spectral Target Recognition in Hyperspectral Imagery [J]. IEEE Transactons on Aerospace and Electronic System, 2003, 39(4): 1232-1249.
|
24 |
Li H C, Chang C I. Recursive Orthogonal Projection-based Simplex Growing Algorithm [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 3780-3793.
|
25 |
Harsanyi J C, Chang C I. Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach [J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 779-785.
|
26 |
Du Qian, Raksuntorn N, Younan N H, et al. Endmember Extraction for Hyperspectral Image Analysis [J]. Applied Optics, 2008, 47(28): 77-84.
|
27 |
Wu Bo, Zhang Liangpei,Li Pingxiang. Unsupervised Orthogonal Subspace Projection Approach to Unmixing Hyperspectral imagery Automatically[J].Journal of Image and Graphics,2004,9(11):1392-1397.吴波,张良培,李平湘.非监督正交子空间投影的高光谱混合像元自动分解[J].中国图象图形学报,2004, 9(11): 1392-1397.
|
28 |
Zhao Yan,Wang Donghui,Zhang Chunjin, et al. An Orthogonal Subspace Extraction Algorithm Projection for Endmember in Hyperspectral Images[J]. Journal of Engineering of Heilongjiang University,2016,7(3):82-86.赵岩,王东辉,张春晶,等.一种正交子空间投影高光谱图像端元提取算法[J].黑龙江大学工程学报,2016, 7(3): 82-86.
|
29 |
Geng Xiurui, Tong Qingxi, Zheng Lanfen. Ground Object Extraction Algorithm of Hyperspectral Imagery based on Endmember Projection Vector[J]. Progress in Natural Science,2005,15(4):509-512.耿修瑞,童庆禧,郑兰芬.一种基于端元投影向量的高光谱图像地物提取算法[J].自然科学进展, 2005, 15(4): 509-512.
|
30 |
Luo Wenfei,Zhong Liang,Zhang Bing,et al. Null Space Spectral Projection Algorithm for Hyperspectral Image Endmember Extraction[J].Journal of Infrared Millimeter Waves,2010,29(4):307-311.罗文斐,钟亮,张兵,等.高光谱遥感图像端元提取的零空间光谱投影算法[J].红外与毫米波学报,2010, 29(4): 307-311.
|
31 |
Tao Xuetao, Wang Bin, Zhang Liming. Orthogonal bases Approach for The Decomposition of Mixed Pixels in Hyperspectral Imagery [J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 219-223.
|
32 |
Tao Xuetao, Wang Bin, Zhang Liming. A New Unminxing Method for Remote Sensing Image based on Orthgonal base on Data Space[J]. China Science: Information Science, 2009,39(4):454-467.陶雪涛,王斌,张立明.基于数据空间正交基的遥感图像混合像元分解新方法[J].中国科学:信息科学,2009,39(4): 454-467.
|
33 |
Song Meiping, Xu Xingwei, Chang C I. et al. Orthogonal Vector Projection Algorithm for Spectral Unmixing[J]. Spectroscopy and Spectral Analysis,2015,35(12):3465-3470.宋梅萍,徐行伟, Chein I,等.用于光谱解混的正交向量投影算法[J].光谱学与光谱分析, 2015, 35(12): 3465-3470.
|
34 |
Heylen R, Burazerovic D, Scheunders P. Fully Constrained Least Squares Spectral Unmixing by Simplex Projection [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4112-4122.
|
35 |
Guerra R, Santos L, Lopez S, et al. A New Fast Algorithm for Linearly Unmixing Hyperspectral Images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12): 6752-6765.
|
36 |
Chang C I, Li H C, Wu C C, et al. Recursive Geometric Simplex Growing Analysis for Finding Endmembers in Hyperspectral Imagery [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(1): 296-308.
|
37 |
Li H C. Geometric N-Finder Algorithm for Finding Endmembers in Hyperspectral Imagery [C]∥IEEE Geoscience and Remote Sensing Society. Fort Worth, Texas, USA. 2017.
|
38 |
Winter M E. N-FINDR: An Algorithm for Fast Autonomous Spectral Endmember Determination in Hyperspectral Data [C]∥Proceedings of Society of Photo-Optical Instrumentation Engineers Conference of Imaging Spectrometry V, Denver, CO,1999.
|
39 |
Chang C I, Wu C C, Liu W M, et al. A New Growing Method for Simplex-based Endmember Extraction Algorithm [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2804-2819.
|
40 |
Wang L G, Jia X P, Zhang Y. A Novel Geometry-based Feature-selection Technique for Hyperspectral Imagery [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(1): 171-175.
|
41 |
Wu C C, Chu S, Chang C I. Sequential N-FINDR algorithm [C]∥Society of Photo-optical Instrumentation Engineers Conference of Imaging Spectrometry XIII. San Diego, CA. 2008.
|
42 |
Wang Y, Guo L, Liang N.Using a New Search Strategy to Improve the Performance of N-FINDR Algorithm for End-Member Determination [C]∥Proceedings of the 2009 2nd International Congress on Image and Signal Processing,Tianjing, China. 2009.
|
43 |
Xiong W, Wu C C, Chang C I, et al. Fast Algorithms to Implement N-FINDR for Hyperspectral Endmember Extraction [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 4(3): 545-564.
|
44 |
Chang C I, Wu C C, Tsai C T. Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery [J]. IEEE Transactions on Image Processing, 2011, 20(3): 641-656.
|
45 |
Dowler S W, Rakashima R, Andrews M. Reducing the Complexity of the N-FINDR Algorithm for Hyperspectral Image Analysis [J]. IEEE Transactions on Image Processing, 2013, 22 (7): 2835-2848.
|
46 |
Ji L Y, Geng X R, Sun K, et al. Modified N-FINDR Endmember Extraction Algorithm for Remote Sensing Imagery [J]. International Journal of Remote Sensing, 2015, 36(8): 2148-2162.
|
47 |
Li Lin,Meng Lingbo,Sun Kang, et al.A Fast N-FINDR Algorithm based on Cofactor of a Determinant[J].Journal of Electronics & Information Technology,2015,37(5):1128-1134.李琳,孟令博,孙康,等.基于代数余子式的N-FINDR快速端元提取算法[J]. 电子与信息学报, 2015, 37(5): 1128-1134.
|
48 |
Zhao Chunhui,Guo Yunting. An Improved Fast N-FINDR Endmember Extraction Algorithm[J]. Acta Photonica Sinica,2015,44(10):1-8.赵春晖,郭蕴霆.一种改进的快速N-FINDR端元提取算法[J].光子学报, 2015, 44(10): 1-8.
|
49 |
Zortea M, Plaza A. A Quantitative and Comparative Analysis of Different Implementations of N-FINDR: A Fast Endmember Extraction Algorithm [J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4): 787-791.
|
50 |
Zhao Chunhui,Qi Bin,Wang Yulei.An Improved N-FINDR Hyperspectral Endmember Extraction Algorithm[J].Journal of Electronics & Information Technology,2012,34(2):499-503.赵春晖,齐滨,王玉磊.一种改进的N-FINDR高光谱端元提取算法[J].电子与信息学报,2012, 34(2): 499-503.
|
51 |
Chang C I, Wu C C, Lo C S, et al. Real-Time Simplex Growing Algorithms for Hyperspectral Endmember Extraction [J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4): 1834-1850.
|
52 |
Pu Hanye,Wang Bin,Zhang Liming.Cayley-menger Determinant based Endmember Extraction Algorithm for Hyperspectral Unmixing[J].Journal of Infrared Millimeter Waves,2012,31(3):265-270.普晗晔,王斌,张立明.基于Cayley-Menger行列式的高光谱遥感图像端元提取方法[J].红外与毫米波学报,2012, 31(3): 265-270.
|
53 |
Xia W, Pu H Y, Wang B, et al. Triangular Factorization based Simplex Algorithms for Hyperspectral Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4420-4440.
|
54 |
Schultz R C, Hobbs M, Chang C-I. Progressive Band Processing of Simplex Growing Algorithm for Finding Endmembers in Hyperspectral Imagery [C]∥SPIE Satellite Data Compression, Communications, and Processing X, 2014.
|
55 |
Sun K,Geng X R,Wang P S, et al. A Fast Endmember Extraction Algorithm based on Gram Determinant [J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1124-1128.
|
56 |
Wang Lijiao, Li Xiaoren, Zhao Liaoying. Fast Implement of The Simplex Growing Algorithm for Endmember Extraction[J]. Acta Optica Sinica, 2014, 34(11):1-7.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): 1-7.
|
57 |
Wang Liguo, Zhang Jing, Liu Danfeng, et al. Distance Measurement based Methods from Endmember Selection to Spectral Unmixing[J]. Journal of Infrared Millimeer Waves,2010, 29(6):471-475.王立国,张晶,刘丹凤,等.从端元选择到光谱解混的距离测算方法[J].红外与毫米波学报,2010, 29(6): 471-475.
|
58 |
Zhao L Y, Zheng J P, Li X R, et al. Kernel Simplex Growing Algorithm for Hyperspectral Endmember Extraction [J]. Journal of Applied Remote Sensing, 2014, 8(1): 083594-1-15.
|
59 |
Luo Wenfei, Zhong Liang, Liu Xiang, et al. Endmember Extraction Algorithm of Hyperspectral Imagery based on Maximum Distance of Null Space[J]. Progress in Natural Science,2008,18(11):1341-1345.罗文斐,钟亮,刘翔,等.基于零空间最大距离的高光谱图像端元提取算法[J].自然科学进展,2008, 18(11): 1341-1345.
|
60 |
Craig M D. Minimum-volume Transforms for Remotely Sensed Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(1): 542-552.
|
61 |
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.
|
62 |
T-H Chan,Chi C-Y,Huang Y M, et al. A Convex Analysis-based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing [J]. IEEE Transactions on Signal Processing, 2009, 57(11): 4418-4432.
|
63 |
Li J, Bioucas-Dias J M. Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data [C]∥Proceedings IEEE International Geoscience and Remote Sensing Symposium, Boston, 2008.
|
64 |
Li J, Agathos A, Zaharie D, et al. Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 5067-5082.
|
65 |
Ambikapathi A, Chan T H, Ma W K, et al. Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4194-4209.
|
66 |
Hendrix E T, Garcia I, Plaza J, et al. A New Minimum-Volume Enclosing Algorithm for Endmember Identification and Abundance Estimation in Hyperspectral Data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7): 2744-2757.
|
67 |
Lin C H, Chi C Y, Wang Y H, et al. A Fast Hyperplane-based Minimum-Volume Enclosing Simplex Algorithm for Blind Hyperspectral Unmixing [J]. IEEE Transactions on Signal Processing, 2016, 64(8): 1946-1961.
|
68 |
Zhang S Q, Agathos A, Li J. Robust Minimum Volume Simplex Analysis for Hyperspectral Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11): 6431-6439.
|
69 |
Wang Tiancheng,Liu Xiangzhen,Dong Zezheng,et al. A Robust Minimum Volume based Algorithm with Automatically Estimating Regularization Parameters for Hyperspectral Unmixing[J].Acta Automatica Sinica,2017,43(12):2141-2159.王天成,刘相振,董泽政,等.一种自适应鲁棒最小体积高光谱解混算法[J].自动化学报,2017,43(12):2141-2159.
|
70 |
Geng Xiurui, Zhao Yongchao, Zhou Guanhua. An Automatic Endmember Extraction Algorithm of Hyperspectral Imagery based on Simplex Volume[J]. Progress in Natural Science, 2006,16(9):1196-1200.耿修瑞,赵永超,周冠华.一种利用单形体体积自动提取高光谱图像端元的算法[J].自然科学进展,2006, 16(9): 1196-1200.
|
71 |
Geng Xiurui, Zhang Bing, Zhang Xia, et al. An Unmixing Method of Hyperspectral Imagery based on Convex Volume in High Dimensional Space[J]. Progress in Nature Sciences, 2004,14(7):810-814.耿修瑞,张兵,张霞,等.一种基于高维空间凸面单形体体积的高光谱图像解混算法[J].自然科学进展,2004,14(7): 810-814.
|
72 |
Honeine P, Richard C. Geometric Unmixing of Large Hyperspectral Images: A Barycentric Coordinate Approach [J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(6): 2185-2195.
|
73 |
Yang H D, An J B, Zhu C. Subspace-Projection-based Geometric Unmixing for Material Quantification in Hyperspectral Imagery [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 1966-1975.
|
74 |
Luo Wenfei,Zhong Liang,Zhang Bing,et al. A Spectral Unmixing Algorithm for Hyperspectral Image based on the Distance to Subspace[J]. Progress in Natural Science, 2008,18(10):1175-1180.罗文斐,钟亮,张兵,等.基于子空间距离的高光谱图像光谱解混算法[J].自然科学进展,2008,18(10): 1175-1180.
|
75 |
Geng X R, Ji L Y, Zhao Y C, et al. A New Endmember Generation Algorithm based on a Geometric Optimization Model for Hyperspectral Images [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 811-815.
|
76 |
Geng X R, Sun K, Ji L Y, et al. Optimizing the Endmembers Using Volume Invariant Constrained Model [J]. IEEE Transactions on Image Processing, 2015, 24(11): 3441-3449.
|
77 |
Ifarraguerri A, Chang C I. Multispectral and Hyperspectral Image Analysis with Convex Cones [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2): 756-770.
|
78 |
Gruninger J H, Lee J, Sundberg R L. Application of Convex Cone Analysis to Hyperspectral and Multispectral Scenes [C]∥Proceedings of the SPIE, Image and Signal Processing for Remote Sensing VIII, Agia Pelagia, 2003.
|
79 |
Gruninger J H, Ratkowski A J, Hoke M L. The Sequential Maximum Angle Convex Cone (SMACC) Endmember Model [C]∥Proceedings of the SPIE, Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery X, Orlando2004.
|
80 |
Chu Haifeng,Zhai zhongmin,Zhao Yindi, et al.A Convex Cone Analysis Method for Endmember Selection of Multispectral and Hyperspectral Images[J]. Journal of Remote Sensing,2007,11(4):460-467.褚海峰,翟中敏,赵银娣,等.一种多/高光谱遥感图像端元提取的凸锥分析算法[J].遥感学报,2007,11(4): 460-467.
|
81 |
Zhu Shulong, Qi Jiancheng, Zhu Baoshan, et al. Fast Extraction of Endmembers from Convex Simplex's Boundary[J].Journal of Remote Sensing,2010,14(3):482-492.朱述龙,齐建成,朱宝山,等.以凸面单体边界为搜索空间的端元快速提取算法[J].遥感学报, 2010, 14(3): 482-492.
|
82 |
Solares C. The Gamma Algorithm in Convex Cone Analysis of Hyperspectral Images//Rudas I, Demiralp M, Mastorakis N. Recent Advances in Signals and Systems[M]. Budapest Tech, Hungary; WSEAS Press, 2009.
|
83 |
Xiong W, Tsai C T, Yang C W, et al. Convex Cone-based Endmember Extraction for Hyperspectral Imagery[C]∥Proceedings of the SPIE, Optical Engineering and Applications, San Diego, CA, 2010.
|
84 |
Li H C, Chang C I. Geometric Convex Cone Volume Analysis[C]∥Proceedings of the SPIE, Remotely Sensed Data Compression, Communications, and Processing XII, Baltimore, MD, 2016.
|
85 |
Chang C I. Real-time Progressive Hyperspectral Image Processing-Endmember Finding and Anomaly Detection [M]. USA, Baltimore, Maryland: Springer, 2016.
|
86 |
Xu Ning,Geng Xiurui,You Hongjian, et al.A Fully Constrained Linear Unmixing Method based on Simplex Regularization for Hyperspectral Image[J].Journal of Infrared Millimeer Waves,2016, 35(5):592-599.许宁,耿修瑞,肖新耀,等.一种基于单形体正化的高光谱数据全约束线性解混方法[J].红外与毫米波学报,2016,35(5): 592-599.
|
87 |
Geng X R, Ji L Y, Wang F X, et al. Statistical Volume Analysis: A New Endmember Extraction Method for Multi-hyperspectral Imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6100-6109.
|
88 |
Heinz D C, Chang C I. Fully Constrained Least Squares Linear Mixture Analysis for Material Quantification in Hyperspectral Imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529-545.
|
89 |
Chen X H, Chen J, Jia X P, et al. A Quantitative Analysis of Virtual Endmembers' Increased Impact on the Collinearity Effect in Spectral Unmixing [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8): 2945-2956.
|
90 |
Chan T H, Ma W K, Ambikapathi A, et al. A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4177-4193.
|
91 |
Ready P, Wintz P. Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery [J]. IEEE Transactions on Communications, 1973, 21(10): 1123-1131.
|
92 |
Green A A, Berman M, Switzer P, et al. A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal [J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65-74.
|
93 |
Geng Xiurui. Research on Hyperspectral Remote Sensing Imagery Target Detection and Classification Technology[D].Beijing: Institute of Remote Sensing Application, Chinese Acodemy of Science, 2005.耿修瑞.高光谱遥感图像目标探测与分类技术研究[D].北京:中国科学院研究生院, 2005.
|
94 |
Lin C H,Ma W K, Li W C, et al. Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing-The No-Pure-Pixel Case [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5530-5546.
|
95 |
Geng X R, 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): 1027-1235.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|