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    Journals >Laser & Optoelectronics Progress >Volume 56 >Issue 16 >Page 161001 > Article
    • Laser & Optoelectronics Progress
    • Vol. 56, Issue 16, 161001 (2019)
    Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization
    Shuai Fang1、**, Jinming Wang1、*, and Fengyun Cao2、3
    Author Affiliations
  • 1 Department of Artificial Intelligence and Data Mining, School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui 230601, China
  • 2 Anhui Provincial Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei, Anhui 230601, China
  • 3 School of Computer Science and Technology, Hefei Normal University, Hefei, Anhui 230601, China
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    DOI: 10.3788/LOP56.161001 Cite this Article Set citation alerts
    Shuai Fang, Jinming Wang, Fengyun Cao. Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161001 Copy Citation Text show less
    Procedure of SSPP-CNMF algorithm
    Fig. 1. Procedure of SSPP-CNMF algorithm
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    Hyperspectral images. (a) Fractal1; (b) Jasper; (c) Cuprite
    Fig. 2. Hyperspectral images. (a) Fractal1; (b) Jasper; (c) Cuprite
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    Jasper ground truths (GT) and endmember results obtained by proposed algorithm. (a) Tree; (b) soil; (c) water; (d) road
    Fig. 3. Jasper ground truths (GT) and endmember results obtained by proposed algorithm. (a) Tree; (b) soil; (c) water; (d) road
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    Ground truths of Jasper abundance. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 4. Ground truths of Jasper abundance. (a) Water; (b) soil; (c) road; (d) tree
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    Jasper abundances estimated by VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 5. Jasper abundances estimated by VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
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    Jasper abundances estimated by CoNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 6. Jasper abundances estimated by CoNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
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    Jasper abundances estimated by MVC-NMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 7. Jasper abundances estimated by MVC-NMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
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    Jasper abundances estimated by SSPP-VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 8. Jasper abundances estimated by SSPP-VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
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    Jasper abundances estimated by SSPP-CNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
    Fig. 9. Jasper abundances estimated by SSPP-CNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
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    Fractal1 abundances estimated by SSPP-CNMF algorithm. (a) Halloysite; (b) Nontronite; (c) Kaolinite CM9; (d) Sphene; (e) Muscovite; (f) Kaolinite KGa1; (g) Dumortierite; (h) Pyrophyllite; (i) Alunite
    Fig. 10. Fractal1 abundances estimated by SSPP-CNMF algorithm. (a) Halloysite; (b) Nontronite; (c) Kaolinite CM9; (d) Sphene; (e) Muscovite; (f) Kaolinite KGa1; (g) Dumortierite; (h) Pyrophyllite; (i) Alunite
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    Fractal1 ground truth and endmember spectra estimated by SSPP-CNMF algorithm. (a) Dumortierite; (b) Halloysite; (c) Kaolinite CM9; (d) Kaolinite KGa1; (e) Muscovite; (f) Nontronite; (g) Pyrophyllite; (h) Sphene
    Fig. 11. Fractal1 ground truth and endmember spectra estimated by SSPP-CNMF algorithm. (a) Dumortierite; (b) Halloysite; (c) Kaolinite CM9; (d) Kaolinite KGa1; (e) Muscovite; (f) Nontronite; (g) Pyrophyllite; (h) Sphene
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    Cuprite abundances estimated by SSPP-CNMF algorithm. (a) Endmember 1;(b) Endmember 2; (c) Endmember 3; (d) Endmember 4; (e) Endmember 5; (f) Endmember 6; (g) Endmember 7; (h) Endmember 8; (i) Endmember 9; (j) Endmember 10; (k) Endmember 11; (l) Endmember 12
    Fig. 12. Cuprite abundances estimated by SSPP-CNMF algorithm. (a) Endmember 1;(b) Endmember 2; (c) Endmember 3; (d) Endmember 4; (e) Endmember 5; (f) Endmember 6; (g) Endmember 7; (h) Endmember 8; (i) Endmember 9; (j) Endmember 10; (k) Endmember 11; (l) Endmember 12
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    Hyperspectral datasetsSNR /dBSAD /10-2
    VCAMVSAMVC-NMFCoNMFSSPP-VCAOurs
    Fractal13020.3222.4617.519.6811.379.31
    Jasper41.6328.4224.4229.4312.8212.71
    Cuprite25.0812.4220.2219.7113.3213.22
    Table 1. Comparison of SAD of different hyperspectral unmixing algorithms
    Hyperspectral datasetsSNR /dBRMSE /10-2
    VCAMVSAMVC-NMFCoNMFSSPP-VCAOurs
    Fractal13024.1922.7115.3710.6813.4410.52
    Jasper36.6138.8119.7526.7219.3918.78
    Cuprite——————
    Table 2. Comparison of RMSE of different hyperspectral unmixing algorithms
    Abstract
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    Shuai Fang, Jinming Wang, Fengyun Cao. Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161001
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