T. Aach and A. Kaup, Statistical model-based change detection in moving video, Signal Processing, vol.31, issue.2, p.165180, 1993.
DOI : 10.1016/0165-1684(93)90063-G

R. J. Alder, The Geometry of Random Fields, 1981.

R. Balkrishnan, A. J. Mcmichael, F. T. Camacho, F. Saltzberg, T. S. Housman et al., Development and validation of a health-related quality of life instrument for women with melasma, British Journal of Dermatology, vol.149, p.572577, 2003.

J. E. Ball, L. M. Bruce, and N. H. Younan, Hyperspectral pixel unmixing via spectral band selection and DC-insensitive singular value decomposition. Geoscience and Remote Sensing Letters, 2007.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, Classication of hyperspectral data from urban areas based on extended morphological proles, IEEE Trans. on Geoscience and Remote Sensing, vol.40, p.480491, 2005.

J. Bezdek, Pattern recognition with fuzzy objective functions, 1981.
DOI : 10.1007/978-1-4757-0450-1

J. W. Boardman, F. A. Kruse, and R. O. Green, Mapping target signatures via partial unmixing of AVIRIS data, Summaries of JPL Airborne Earth Science Workshop, 1995.

L. Bruzzone and D. F. Prieto, An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images, IEEE Transactions on Image Processing, vol.11, issue.4, p.452466, 2002.
DOI : 10.1109/TIP.2002.999678

J. F. Cardoso, High-Order Contrasts for Independent Component Analysis, Neural Computation, vol.140, issue.1, p.157192, 1999.
DOI : 10.1109/78.599941

G. Celeux and G. Govaert, A classication EM algorithm for clustering and two stochastic versions, Comput. Stat. Data Anal, vol.14, p.315332, 1992.

C. Chang, C. C. Wu, and C. T. Tsai, Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery, IEEE Transactions on Image Processing, vol.20, issue.3, p.641656, 2011.
DOI : 10.1109/TIP.2010.2071310

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5
DOI : 10.1109/34.1000236

A. Cornuéjols and L. Miclet, L'apprentissage articiel concepts et algorithmes 2ème édition. Eyrolles, 2009.

I. S. Dhillon and S. Sra, Generalized nonnegative matrix approximations with Bregman divergences, Neural Information Proc. Systems, 2005.

N. Dobigeon, S. Moussaoui, M. Coulon, J. Y. Tourneret, and A. O. Hero, Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.57, issue.11, p.43554368, 2009.
DOI : 10.1109/TSP.2009.2025797
URL : https://hal.archives-ouvertes.fr/hal-00548758

K. J. Friston, J. Ashburner, S. J. Kiebel, T. E. Nichols, and W. D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images, 2007.

A. A. Green, M. Berman, P. Switzer, and M. D. Craig, A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, vol.26, issue.1, 1988.
DOI : 10.1109/36.3001

G. Hazel, Multivariate Gaussian MRF for multispectral scene segmentation and anomaly detection, IEEE Transactions on Geoscience and Remote Sensing, vol.38, issue.3, p.11991211, 2000.
DOI : 10.1109/36.843012

G. F. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, p.5563, 1968.
DOI : 10.1109/TIT.1968.1054102

M. E. Hurley, I. L. Guevara, R. M. Gonzales, and A. G. Pandya, Ecacy of glycolic acid peels in the treatment of melasma, Archives of Dermatology, vol.138, p.15781582, 2002.

A. Hyvarinen, Fast and robust xed-point algorithms for independent component analysis, IEEE Trans. on Neural Networks, vol.10, p.626634, 1999.

J. Inglada and G. Mercier, A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.5, p.14321445, 2007.
DOI : 10.1109/TGRS.2007.893568
URL : https://hal.archives-ouvertes.fr/hal-00582539

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, p.264323, 1999.
DOI : 10.1145/331499.331504

. Velez, Integration of spatial and spectral information by means of unsupervised extraction and classication for homogenous objects applied to multispectral and hyperspectral data

C. Kimbrough-green, . Griths, T. Finkel, S. Hamilton, C. Bulengo-ransby et al., Topical retinoic acid (tretinoin) for melasma in black patients. a vehiclecontrolled clinical trial, RR Arch Dermatol, vol.874526, issue.130, p.72733, 1994.

D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, 2003.
DOI : 10.1002/0471723800

C. Lee and D. A. Landgrebe, Feature extraction based on decision boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.4, p.388400, 1993.
DOI : 10.1109/34.206958

C. Lee and D. A. Landgrebe, Decision boundary feature extraction for neural networks, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics, p.7583, 1997.
DOI : 10.1109/ICSMC.1992.271652

D. D. Lee and H. S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature, vol.401, p.788791, 1999.

L. Li and M. K. Leung, Integrating intensity and texture dierences for robust change detection, IEEE Trans. Image Process, vol.11, p.105112, 2002.

S. Liu, C. Fu, and S. Chang, Statistical change detection with moments under time-varying illumination, IEEE Trans. Image Process, vol.7, p.12581268, 1998.

D. Lu, P. Mausel, E. Brondízio, and E. Moran, Change detection techniques, International Journal of Remote Sensing, vol.66, issue.12, p.23652401, 2004.
DOI : 10.1659/0276-4741(2001)021[0175:LCCATA]2.0.CO;2

R. M. Manaloto and T. Alster, Erbium:YAG Laser Resurfacing for Refractory Melasma, Dermatologic Surgery, vol.25, issue.2, p.121123, 1999.
DOI : 10.1046/j.1524-4725.1999.08103.x

P. Mather, Computer processing of remotely???sensed images ??? An introduction, Geocarto International, vol.2, issue.4, 2004.
DOI : 10.1080/10106048709354125

A. M. Mood, F. A. Graybill, and D. C. Boes, Introduction to the theory of statistics, 1974.

A. Myronenko and X. Song, Intensity-Based Image Registration by Minimizing Residual Complexity, IEEE Transactions on Medical Imaging, vol.29, issue.11, p.18821891, 2010.
DOI : 10.1109/TMI.2010.2053043

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, issue.4, p.898910, 2005.
DOI : 10.1109/TGRS.2005.844293

P. Paatero and U. Tapper, Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.18, issue.2, p.111126, 1994.
DOI : 10.1002/env.3170050203

A. Pandya, M. Berneburg, J. Ortonne, and M. Picardo, Guidelines for clinical trials in melasma. pigmentation disorders academy, Br J Dermatol, vol.156, issue.1, 2006.

A. G. Pandya, L. S. Hynan, R. Bhore, F. C. Riley, I. L. Guevara et al., Reliability assessment and validation of the Melasma Area and Severity Index (MASI) and a new modified MASI scoring method, Journal of the American Academy of Dermatology, vol.64, issue.1, pp.78-83, 2011.
DOI : 10.1016/j.jaad.2009.10.051

A. Plaza and C. I. Chang, A Fast Iterative Algorithm for Implementation of Pixel Purity Index, IEEE Geoscience and Remote Sensing Letters, vol.3, issue.1, p.6367, 2006.

A. Plaza, P. Martinez, R. Perez, and J. Plaza, Spatial/spectral endmember extraction by multidimensional morphological operations, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.9, p.20252041, 2002.
DOI : 10.1109/TGRS.2002.802494

J. B. Poline, K. J. Worsley, A. C. Evans, and K. J. Friston, Combining Spatial Extent and Peak Intensity to Test for Activations in Functional Imaging, NeuroImage, vol.5, issue.2, p.8396, 1997.
DOI : 10.1006/nimg.1996.0248

H. V. Poor, An Introduction to Signal Detection and Estimation, 1994.

S. Prigent, X. Descombes, D. Zugaj, P. Martel, and J. Zerubia, Multi-spectral image analysis for skin pigmentation classication, Proc. IEEE International Conference on Image Processing (ICIP), 2010.

S. Prigent, D. Zugaj, X. Descombes, P. Martel, and J. Zerubia, Estimation of an optimal spectral band combination to evaluate skin disease treatment eciency using multi-spectral images, Proc. IEEE International Conference on Image Processing (ICIP), 2011.

S. Prigent, X. Descombes, D. Zugaj, L. Petit, A. Dugaret et al., Classication of skin hyper-pigmentation lesions with multi-spectral images, 2012.

S. Prigent, X. Descombes, D. Zugaj, L. Petit, A. Dugaret et al., Skin lesion evaluation from multispectral images, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00757039

R. J. Radke, S. Andra, O. Kofahi, and B. Roysam, Image change detection algorithms: a systematic survey, IEEE Transactions on Image Processing, vol.14, issue.3, pp.294-307, 2005.
DOI : 10.1109/TIP.2004.838698

G. Rellier, X. Descombes, F. Falzon, and J. Zerubia, Texture feature analysis using a Gauss-Markov model in hyperspectral image classication, IEEE Trans. on Geoscience and Remote Sensing, vol.42, p.15431551, 2004.

M. Riedmann and E. J. Milton, Supervised band selection for optimal use of data from airborne hyperspectral sensors, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2003.
DOI : 10.1109/IGARSS.2003.1294245

E. Rignot and J. Van-zyl, Change detection techniques for ERS-1 SAR data, IEEE Transactions on Geoscience and Remote Sensing, vol.31, issue.4, p.896906, 1993.
DOI : 10.1109/36.239913

K. Skifstad and R. Jain, Illumination independent change detection for real world image sequences, Comput. Vis. Graph. Image Process, vol.46, p.387399, 1989.

P. Soille, Morphological Image Analysis: Principles and Applications, 2003.

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, Non-Invasive Measurements of Skin Pigmentation In Situ, Pigment Cell Research, vol.4959, issue.6, p.618626, 2004.
DOI : 10.1016/S1076-0512(97)00097-6

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, In vivo measurement of skin erythema and pigmentation: new means of implementation of diuse reectance spectroscopy with a commercial instrument Spectral-spatial classication of hyperspectral imagery based on partitional clustering techniques, British Journal of Dermatology IEEE Trans. Geos. and Remote Sens, vol.159, issue.8, pp.683690-4729732987, 2008.

J. C. Tilton, Image segmentation by region growing and spectral clustering with a natural convergence criterion, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174), 1998.
DOI : 10.1109/IGARSS.1998.703645

S. Valero, P. Salembier, and J. Chanussot, New hyperspectral data representation using binary partition tree, 2010 IEEE International Geoscience and Remote Sensing Symposium, p.8083, 2010.
DOI : 10.1109/IGARSS.2010.5649780
URL : https://hal.archives-ouvertes.fr/hal-00578960

S. Van-der-linden, A. Janz, B. Waske, M. Eiden, and P. Hostert, Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines, Journal of Applied Remote Sensing, vol.1, issue.1, 2007.
DOI : 10.1117/1.2813466

V. Vapnik, Statistical learning theory John Wiley and sons, inc, 1998.

M. Winter, Fast autonomous spectral end-member determination in hyperspectral data, Conf. on Applied Geologic Remote Sensing, p.337344, 1999.

Y. Yakimovsky, Boundary and object detection in real world images, J. ACM, vol.23, p.599618, 1976.

Z. Zhang and X. Huang, Object-oriented subspace analysis for airbone hyperspectral remote sensing imagery, Inria RESEARCH CENTRE SOPHIA ANTIPOLIS ? MÉDITERRANÉE 2004 route des Lucioles -BP 93, p.927936, 2010.