1. [1] R. A. Schowengerdt, Remote sensing: models and methods for image processing. Academic press, 2006.
2. [2] C. HEINZD, "FullyConstrainedLeastSquares LinearMixture Analysisfor Material Quantific-ationin Hyperspectral Imagery," IEEE Transac-tionson Geoscience and Remote Sensing, vol. 39, no. 3, p. 529, 2001.
3. [3] A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote sensing of environment, vol. 113, pp. S110-S122, 2009. [
DOI:10.1016/j.rse.2007.07.028]
4. [4] J. M. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 2, pp. 354-379, 2012. [
DOI:10.1109/JSTARS.2012.2194696]
5. [5] P. E. Dennison and D. A. Roberts, "Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE," Remote sensing of environment, vol. 87, no. 2, pp. 123-135, 2003. [
DOI:10.1016/S0034-4257(03)00135-4]
6. [6] J. Settle, "On the effect of variable endmember spectra in the linear mixture model," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 2, pp. 389-396, 2006. [
DOI:10.1109/TGRS.2005.860983]
7. [7] B. Somers, G. P. Asner, L. Tits, and P. Coppin, "Endmember variability in spectral mixture analysis: A review," Remote Sensing of Environment, vol. 115, no. 7, pp. 1603-1616, 2011. [
DOI:10.1016/j.rse.2011.03.003]
8. [8] L. Wang and X. Jia, "Integration of soft and hard classifications using extended support vector machines," IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 3, pp. 543-547, 2009. [
DOI:10.1109/LGRS.2009.2020924]
9. [9] M.-D. Iordache, "A sparse regression approach to hyperspectral unmixing," INSTITUTO SUPERIOR TÉCNICO, 2011.
10. [10] J. W. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," 1993.
11. [11] M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, pp. 266-275: International Society for Optics and Photonics. [
DOI:10.1117/12.366289]
12. [12] R. Neville, "Automatic endmember extraction from hyperspectral data for mineral exploration," in International Airborne Remote Sensing Conference and Exhibition, 4 th/21 st Canadian Symposium on Remote Sensing, Ottawa, Canada, 1999. [
DOI:10.4095/219526]
13. [13] J. M. Nascimento and J. M. Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE transactions on Geoscience and Remote Sensing, vol. 43, no. 4, pp. 898-910, 2005. [
DOI:10.1109/TGRS.2005.844293]
14. [14] C.-I. Chang, C.-C. Wu, W. Liu, and Y.-C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2804-2819, 2006. [
DOI:10.1109/TGRS.2006.881803]
15. [15] J. H. Gruninger, A. J. Ratkowski, and M. L. Hoke, "The sequential maximum angle convex cone (SMACC) endmember model," in Defense and Security, 2004, pp. 1-14: International Society for Optics and Photonics.
16. [16] T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, "A simplex volume maximization framework for hyperspectral endmember extraction," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4177-4193, 2011. [
DOI:10.1109/TGRS.2011.2141672]
17. [17] M. Möller, E. Esser, S. Osher, G. Sapiro, and J. Xin, "A convex model for matrix factorization and dimensionality reduction on physical space and its application to blind hyperspectral unmixing," DTIC Document2010. [
DOI:10.21236/ADA540658]
18. [18] J. H. Bowles, P. J. Palmadesso, J. A. Antoniades, M. M. Baumback, and L. J. Rickard, "Use of filter vectors in hyperspectral data analysis," in SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, pp. 148-157: International Society for Optics and Photonics.
19. [19] A. Ifarraguerri and C.-I. Chang, "Multispectral and hyperspectral image analysis with convex cones," IEEE Transactions on Geoscience and Remote Sensing, vol. 37, no. 2, pp. 756-770, 1999. [
DOI:10.1109/36.752192]
20. [20] A. Plaza, P. Martínez, R. Pérez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE transactions on geoscience and remote sensing, vol. 40, no. 9, pp. 2025-2041, 2002. [
DOI:10.1109/TGRS.2002.802494]
21. [21] M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. F. Huntington, "ICE: A statistical approach to identifying endmembers in hyperspectral images," IEEE transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2085-2095, 2004. [
DOI:10.1109/TGRS.2004.835299]
22. [22] L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 3, pp. 765-777, 2007. [
DOI:10.1109/TGRS.2006.888466]
23. [23] D. Rogge, B. Rivard, J. Zhang, A. Sanchez, J. Harris, and J. Feng, "Integration of spatial–spectral information for the improved extraction of endmembers," Remote Sensing of Environment, vol. 110, no. 3, pp. 287-303, 2007. [
DOI:10.1016/j.rse.2007.02.019]
24. [24] A. Zare and P. Gader, "Sparsity promoting iterated constrained endmember detection in hyperspectral imagery," IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 3, pp. 446-450, 2007. [
DOI:10.1109/LGRS.2007.895727]
25. [25] J. Li and J. M. Bioucas-Dias, "Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data," in Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, 2008, vol. 3, pp. III-250-III-253: IEEE. [
DOI:10.1109/IGARSS.2008.4779330]
26. [26] J. M. Bioucas-Dias, "A variable splitting augmented Lagrangian approach to linear spectral unmixing," in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS'09. First Workshop on, 2009, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2009.5289072]
27. [27] A. Zare and P. Gader, "Piece-wise convex spatial-spectral unmixing of hyperspectral imagery using possibilistic and fuzzy clustering," in Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, 2011, pp. 741-746: IEEE. [
DOI:10.1109/FUZZY.2011.6007622]
28. [28] A. Zare, O. Bchir, H. Frigui, and P. Gader, "Spatially-smooth piece-wise convex endmember detection," in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on, 2010, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2010.5594897]
29. [29] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Dictionary pruning in sparse unmixing of hyperspectral data," in Hyperspectral Image and Signal Processing (WHISPERS), 2012 4th Workshop on, 2012, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2012.6874329]
30. [30] M.-D. Iordache, J. M. Bioucas-Dias, A. Plaza, and B. Somers, "MUSIC-CSR: Hyperspectral unmixing via multiple signal classification and collaborative sparse regression," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 7, pp. 4364-4382, 2014. [
DOI:10.1109/TGRS.2013.2281589]
31. [31] M.-D. Iordache, J. Bioucas-Dias, and A. Plaza, "Unmixing sparse hyperspectral mixtures," in Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, 2009, vol. 4, pp. IV-85-IV-88: IEEE. [
DOI:10.1109/IGARSS.2009.5417368]
32. [32] J. M. Bioucas-Dias and M. A. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing," in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on, 2010, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2010.5594963]
33. [33] M.-D. Iordache, A. Plaza, and J. Bioucas-Dias, "On the use of spectral libraries to perform sparse unmixing of hyperspectral data," in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on, 2010, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2010.5594888]
34. [34] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 2014-2039, 2011. [
DOI:10.1109/TGRS.2010.2098413]
35. [35] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Collaborative sparse regression for hyperspectral unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 341-354, 2014. [
DOI:10.1109/TGRS.2013.2240001]
36. [36] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Total variation spatial regularization for sparse hyperspectral unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 11, pp. 4484-4502, 2012. [
DOI:10.1109/TGRS.2012.2191590]
37. [37] X.-L. Zhao, F. Wang, T.-Z. Huang, M. K. Ng, and R. J. Plemmons, "Deblurring and sparse unmixing for hyperspectral images," IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4045-4058, 2013. [
DOI:10.1109/TGRS.2012.2227764]
38. [38] M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Hyperspectral unmixingwith sparse group lasso," in Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, 2011, pp. 3586-3589: IEEE. [
DOI:10.1109/IGARSS.2011.6049999]
39. [39] J. Bieniarz, R. Müller, X. Zhu, and P. Reinartz, "Sparse approximation, coherence and use of derivatives in hyperspectral unmixing," 2012.
40. [40] K. E. Themelis, A. A. Rontogiannis, and K. Koutroumbas, "Sparse semi-supervised hyperspectral unmixing using a novel iterative bayesian inference algorithm," in Signal Processing Conference, 2011 19th European, 2011, pp. 1165-1169: IEEE.
41. [41] D. R. Thompson, R. Castano, and M. S. Gilmore, "Sparse superpixel unmixing for exploratory analysis of CRISM hyperspectral images," in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS'09. First Workshop on, 2009, pp. 1-4: IEEE. [
DOI:10.1109/WHISPERS.2009.5289045]