Hyperspectral Unmixing Method Based on Minimum Volume Sparse Regularization

Fig. 6. Elementary abundance maps of synthetic data SYN1.(a) Endmember 1; (b) endmember 2; (c) endmember 3; (d) endmember 4; (e) endmember 5; (f) endmember 6

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Fig. 7. Elementary abundance maps of synthetic data SYN2. (a) Endmember 1; (b) endmember 2; (c) endmember 3; (d) endmember 4; (e) endmember 5; (f) endmember 6

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Fig. 8. Urban scene image

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Fig. 9. Elementary abundance maps of synthetic data SYN1. (a) Original image; (b) FMVSA algorithm; (c) SISAL algorithm; (d) MVC-NMF algorithm; (e) CoNMF algorithm; (f) MVSR-NMF algorithm

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Fig. 10. Elementary abundance maps of synthetic data SYN2. (a) Original image; (b) FMVSA algorithm; (c) SISAL algorithm; (d) MVC-NMF algorithm; (e) CoNMF algorithm; (f) MVSR-NMF algorithm

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Fig. 11. Abundances of different algorithms. (a) Original image; (b) FMVSA algorithm; (c) SISAL algorithm; (d) MVC-NMF algorithm; (e) CoNMF algorithm; (f) MVSR-NMF algorithm

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Fig. 12. Comparison of endmember spectral bands under different algorithms. (a) Original endmember spectral bands; (b) endmember spectral bands of MVC-NMF algorithm; (c) endmember spectral bands of CoNMF algorithm; (d) endmember spectral bands of MVSR-NMF algorithm

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Table 1. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN1in different signal-to-noise ratios

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Table 1. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN1in different signal-to-noise ratios

Algorithm	E_SA /(°)					E_FAA /(°)					t /s
Algorithm	20dB	25dB	30dB	35dB	40dB	20dB	25dB	30dB	35dB	40dB	t /s
FMVSA	6.69	4.63	2.61	1.63	1.03	22.93	16.28	9.34	5.52	3.19	21.55
SISAL	4.43	2.89	1.81	1.18	0.86	15.82	11.21	6.75	5.24	4.35	27.82
MVC-NMF	3.25	1.91	1.03	0.55	0.25	13.87	8.55	4.78	2.63	1.35	2159.97
CoNMF	1.93	1.46	1.02	0.51	0.28	12.47	7.97	5.22	3.28	1.96	127.22
MVSR-NMF	1.48	0.78	0.51	0.33	0.21	12.31	7.43	4.75	2.98	1.84	35.54

Table 2. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN2 in different signal-to-noise ratios

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Table 2. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN2 in different signal-to-noise ratios

Algorithm	E_SA /(°)					E_FAA /(°)					t /s
Algorithm	20dB	25dB	30dB	35dB	40dB	20dB	25dB	30dB	35dB	40dB	t /s
FMVSA	6.82	6.33	2.60	1.61	0.99	30.25	29.35	16.26	10.64	6.37	23.48
SISAL	3.27	2.04	1.55	0.93	0.67	24.79	17.69	11.53	7.32	4.25	33.41
MVC-NMF	2.85	1.73	1.01	0.53	0.24	22.89	16.41	10.24	5.77	2.81	2549.52
CoNMF	1.68	1.13	0.89	0.49	0.35	18.97	14.45	9.98	5.94	3.68	244.27
MVSR-NMF	1.39	0.96	0.64	0.40	0.23	17.78	12.13	10.51	5.39	3.64	55.49

Table 3. Performance comparison of endmember matrix and abundance matrix in different algorithms
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Table 3. Performance comparison of endmember matrix and abundance matrix in different algorithms
Method MVC-NMF FMVSA CoNMF SISAL MVSR-NMF
${\dot{E}}_{MS} (A, \dot{a})$ 2.97 2.91 2.53 2.61 2.48
${\dot{E}}_{MS} (S, \dot{S})$ 1.04 0.87 0.83 0.96 0.81

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Guangxian Xu, Yanwei Wang, Fei Ma, Feixia Yang. Hyperspectral Unmixing Method Based on Minimum Volume Sparse Regularization[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241010

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Paper Information

Category: Image Processing

Received: Apr. 24, 2020

Accepted: Jun. 9, 2020

Published Online: Dec. 30, 2020

The Author Email: Wang Yanwei (wangyw2018@gmail.com), Ma Fei (wangyw2018@gmail.com)

DOI:10.3788/LOP57.241010

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN1in different signal-to-noise ratios

Table 1. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN1in different signal-to-noise ratios

Table 2. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN2 in different signal-to-noise ratios

Table 2. Performance comparison of endmember estimation and abundance estimation of synthetic data SYN2 in different signal-to-noise ratios

Table 3. Performance comparison of endmember matrix and abundance matrix in different algorithms

Table 3. Performance comparison of endmember matrix and abundance matrix in different algorithms