RANDOM-PROJECTION-BASED NONNEGATIVE LEAST SQUARES FOR HYPERSPECTRAL IMAGE UNMIXING

被引：0

作者：

Menon, Vineetha ^{[1
]}

Du, Qian ^{[1
]}

Fowler, James E. ^{[1
]}

机构：

[1] Mississippi State Univ, Dept Elect & Comp Engn, Geosyst Res Inst, Mississippi State, MS 39762 USA

来源：

2016 8TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) | 2016年

关键词：

random projection; nonnegative least squares; hyperspectral unmixing; MATRIX FACTORIZATION; REDUCTION;

D O I：

暂无

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

Nonnegative least squares, a state-of-the-art approach to end-member abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of the abundance estimation, with the Hadamard-based approach slightly outperforming its Gaussian counterpart.

引用

页数：5

共 50 条

[1] Nonlinear unmixing of hyperspectral imagery based on constrained least squares
Pu Han-Ye
Wang Bin
Xia Wei
JOURNAL OF INFRARED AND MILLIMETER WAVES, 2014, 33 (05) : 552 - 559
[2] CLASSIFICATION PERFORMANCE OF RANDOM-PROJECTION-BASED DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGERY
Fowler, James E.
Du, Qian
Zhu, Wei
Younan, Nicolas H.
2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5, 2009, : 3501 - 3504
[3] Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization
Fang Shuai
Wang Jinming
Cao Fengyun
LASER & OPTOELECTRONICS PROGRESS, 2019, 56 (16)
[4] Hyperspectral Image Unmixing with Nonnegative Matrix Factorization
Zdunek, Rafal
2012 INTERNATIONAL CONFERENCE ON SIGNALS AND ELECTRONIC SYSTEMS (ICSES), 2012,
[5] RANDOM-PROJECTION-BASED DIMENSIONALITY REDUCTION AND DECISION FUSION FOR HYPERSPECTRAL TARGET DETECTION
Du, Qian
Fowler, James E.
Ma, Ben
2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2011, : 1790 - 1793
[6] Nonnegative least-squares image deblurring: improved gradient projection approaches
Benvenuto, F.
Zanella, R.
Zanni, L.
Bertero, M.
INVERSE PROBLEMS, 2010, 26 (02)
[7] Linear Spectral Unmixing Using Least Squares Error, Orthogonal Projection and Simplex Volume for Hyperspectral Images
Li, Hsiao-Chi
Chang, Chein-I
2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2015,
[8] Efficient sparse unmixing analysis for hyperspectral imagery based on random projection
Shi, Zhenwei
Liu, Liu
Zhai, Xinya
Jiang, Zhiguo
NEURAL COMPUTING & APPLICATIONS, 2013, 23 (7-8): : 2281 - 2293
[9] Efficient sparse unmixing analysis for hyperspectral imagery based on random projection
Zhenwei Shi
Liu Liu
Xinya Zhai
Zhiguo Jiang
Neural Computing and Applications, 2013, 23 : 2281 - 2293
[10] HYPERSPECTRAL IMAGE INTERPRETATION BASED ON PARTIAL LEAST SQUARES
Santos, Andrey Bicalho
Araujo, Arnaldo de Albuquerque
Schwartz, William Robson
Menotti, David
2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, : 1885 - 1889

← 1 2 3 4 5 →