激光与光电子学进展, 2019, 56 (16): 161001, 网络出版: 2019-08-05
基于约束非负矩阵分解的高光谱图像解混 
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Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization
图 & 表
图 2. 高光谱图像。(a) Fractal1;(b) Jasper;(c) Cuprite
Fig. 2. Hyperspectral images. (a) Fractal1; (b) Jasper; (c) Cuprite
图 3. Jasper的ground truth(GT)和所提方法端元提取结果。(a)树木;(b)土壤;(c)水体;(d)道路
Fig. 3. Jasper ground truths (GT) and endmember results obtained by proposed algorithm. (a) Tree; (b) soil; (c) water; (d) road
图 4. Jasper丰度图的ground truth。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 4. Ground truths of Jasper abundance. (a) Water; (b) soil; (c) road; (d) tree
图 5. VCA算法估算的Jasper丰度图。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 5. Jasper abundances estimated by VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
图 6. CoNMF算法估算的Jasper丰度图。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 6. Jasper abundances estimated by CoNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
图 7. MVC-NMF算法估算的Jasper丰度图。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 7. Jasper abundances estimated by MVC-NMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
图 8. SSPP-VCA算法估算的Jasper丰度图。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 8. Jasper abundances estimated by SSPP-VCA algorithm. (a) Water; (b) soil; (c) road; (d) tree
图 9. SSPP-CNMF算法估算的Jasper丰度图。(a)水体;(b)土壤;(c)道路;(d)树木
Fig. 9. Jasper abundances estimated by SSPP-CNMF algorithm. (a) Water; (b) soil; (c) road; (d) tree
图 10. SSPP-CNMF算法估算的Fractal1的丰度图。(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
图 11. Fractal1的ground truth和SSPP-CNMF算法估算的端元波谱。(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
图 12. SSPP-CNMF算法估算的Cuprite的丰度图。(a)端元1;(b)端元2;(c)端元3;(d)端元4;(e)端元5;(f)端元6;(g)端元7;(h)端元8;(i)端元9;(j)端元10;(k)端元11;(l)端元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
表 1不同高光谱解混合算法之间的SAD比较
Table1. Comparison of SAD of different hyperspectral unmixing algorithms
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表 2不同高光谱解混合算法之间的RMSE比较
Table2. Comparison of RMSE of different hyperspectral unmixing algorithms
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方帅, 王金明, 曹风云. 基于约束非负矩阵分解的高光谱图像解混[J]. 激光与光电子学进展, 2019, 56(16): 161001. 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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