References
1. Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Sig Process Mag 19(1):44–57
2. Plaza A, Martínez P, Pérez R et al (2004) A quantitative and comparative analysis of
endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens
42(3):650–663
3. Neville RA, Staenz K, Szeredi T et al (1999) Automatic endmember extraction from
hyperspectral data for mineral exploration. In: Proceedings of the 21st Canadian symposium.
remote sensing, pp 21–24
4. Jia S, Qian Y (2009) Constrained nonnegative matrix factorization for hyperspectral
unmixing. IEEE Trans Geosci Remote Sens 47(1):161–173
5. Sun X, Cai J, Xu Z et al (2013) Hyperspectral unmixing based on nonnegative matrix
factorization. Opto-Electron Eng 39(12):97–102
6. Bioucas-Dias JM, Plaza A, Dobigeon N et al (2012) Hyperspectral unmixing overview:
geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth
Obs Remote Sens 5(2):354–379
7. Bachmann CM, Ainsworth TL, Fusina RA (2005) Exploiting manifold geometry in
hyperspectral imagery. IEEE Trans Geosci Remote Sens 43(3):441–454
8. Du P, Wang X, Tan K et al (2011) Dimensionality reduction and feature extraction from
hyperspectral remote sensing imagery based on manifold learning. Geomatics Inf Sci Wuhan
Univ 36(2):148–152
9. Tang X, Gao K, Ni G et al (2013) An improved N-FINDR endmember extraction algorithm
based on manifold learning and spatial information. Spectrosc Spectral Anal 33(09):2435–
2440
10. Zhang Z, Zha H (2002) Principal manifolds and nonlinear dimensionality reduction via
tangent space alignment. J Shanghai Univ 8(4):406–424
11. Wang L, Zhang Y, Feng J (2005) On the Euclidean distance of images. IEEE Trans Pattern
Anal Mach Intell 27(8):1334–1339
12. Chen H, Pu H-Y, Wang B et al (2013) Image Euclidean distance-based manifold
dimensionality reduction algorithm for hyperspectral imagery. J Infrared Millimeter Waves
32(5):450–455
13. Winter ME (1999) N-FINDR: an algorithm for fast autonomous spectral endmember
determination in hyperspectral data. In: SPIE’s international symposium on optical science,
engineering, and instrumentation. International Society for Optics and Photonics, pp 266–
275
14. Chang CI, Du Q (2004) Estimation of number of spectrally distinct signal sources in
hyperspectral imagery. IEEE Trans Geosci Remote Sens 42(3):608–619
15. https://engineering.purdue.edu/*biehl/MultiSpec/hyperspectral.html
A Novel Endmember Extraction Method Based on Manifold …
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1. Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Sig Process Mag 19(1):44–57
2. Plaza A, Martínez P, Pérez R et al (2004) A quantitative and comparative analysis of
endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens
42(3):650–663
3. Neville RA, Staenz K, Szeredi T et al (1999) Automatic endmember extraction from
hyperspectral data for mineral exploration. In: Proceedings of the 21st Canadian symposium.
remote sensing, pp 21–24
4. Jia S, Qian Y (2009) Constrained nonnegative matrix factorization for hyperspectral
unmixing. IEEE Trans Geosci Remote Sens 47(1):161–173
5. Sun X, Cai J, Xu Z et al (2013) Hyperspectral unmixing based on nonnegative matrix
factorization. Opto-Electron Eng 39(12):97–102
6. Bioucas-Dias JM, Plaza A, Dobigeon N et al (2012) Hyperspectral unmixing overview:
geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth
Obs Remote Sens 5(2):354–379
7. Bachmann CM, Ainsworth TL, Fusina RA (2005) Exploiting manifold geometry in
hyperspectral imagery. IEEE Trans Geosci Remote Sens 43(3):441–454
8. Du P, Wang X, Tan K et al (2011) Dimensionality reduction and feature extraction from
hyperspectral remote sensing imagery based on manifold learning. Geomatics Inf Sci Wuhan
Univ 36(2):148–152
9. Tang X, Gao K, Ni G et al (2013) An improved N-FINDR endmember extraction algorithm
based on manifold learning and spatial information. Spectrosc Spectral Anal 33(09):2435–
2440
10. Zhang Z, Zha H (2002) Principal manifolds and nonlinear dimensionality reduction via
tangent space alignment. J Shanghai Univ 8(4):406–424
11. Wang L, Zhang Y, Feng J (2005) On the Euclidean distance of images. IEEE Trans Pattern
Anal Mach Intell 27(8):1334–1339
12. Chen H, Pu H-Y, Wang B et al (2013) Image Euclidean distance-based manifold
dimensionality reduction algorithm for hyperspectral imagery. J Infrared Millimeter Waves
32(5):450–455
13. Winter ME (1999) N-FINDR: an algorithm for fast autonomous spectral endmember
determination in hyperspectral data. In: SPIE’s international symposium on optical science,
engineering, and instrumentation. International Society for Optics and Photonics, pp 266–
275
14. Chang CI, Du Q (2004) Estimation of number of spectrally distinct signal sources in
hyperspectral imagery. IEEE Trans Geosci Remote Sens 42(3):608–619
15. https://engineering.purdue.edu/*biehl/MultiSpec/hyperspectral.html
A Novel Endmember Extraction Method Based on Manifold …
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