[1]   A. Abdellaoui and A. Rougab. Caractérisation de la reponse du bâti: application au complexe urbain de Blida (Algérie). In Télédétection des milieux urbains et périurbains, AUPELF - UREF, Actes des sixièmes Journées scientifiques du réseau Télédétection de l’AUF, pages 47–64, 1997.

[2]   A. Alexandrescu. Modern C++ Design: Generic Programming and Design Patterns Applied. Professional Computing Series. Addison-Wesley, 2001.

[3]   M. G. e. J. B.-T. Alexis Huck. Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data.

[4]   E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. In Proc. 17th International Conf. on Machine Learning, pages 9–16. Morgan Kaufmann, San Francisco, CA, 2000.

[5]   K. Alsabti, S. Ranka, and V. Singh. An efficient k-means clustering algorithm. In First Workshop on High-Performance Data Mining, 1998.

[6]   L. Alvarez and J.-M. Morel. A Morphological Approach To Multiscale Analysis: From Principles to Equations, pages 229–254. Kluwer Academic Publishers, 1994.

[7]   M. H. Austern. Generic Programming and the STL:. Professional Computing Series. Addison-Wesley, 1999.

[8]   E. Baret and G. Guyot. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35:161–173, 1991.

[9]   E. Baret, G. Guyot, and D. J. Major. TSAVI: A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In Proceedings of the 12th Canadian Symposium on Remote Sensing, Vancouver, Canada, pages 1355–1358, 1989.

[10]   H. Bay, T. Tuytelaars, and L. V. Gool. SURF: Speeded Up Robust Features. Lecture Notes in Computer Science, 3951:404–417, 2006.

[11]   Y. Bazi, L. Bruzzone, and F. Melgani. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geoscience and Remote Sensing, 43(4):874–887, April 2005.

[12]   J. Besag. On the statistical analysis of dirty pictures. J. Royal Statist. Soc. B., 48:259–302, 1986.

[13]   J. 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, pages 1 –4, aug. 2009.

[14]   G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.

[15]   L. Bruzzone and F. Melgani. Support vector machines for classification of hyperspectral remote-sensing images. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS, volume 1, pages 506–508, June 2002.

[16]   L. Bruzzone and D. F. Prieto. An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images. IEEE Trans. Image Processing, 11(4):452–466, April 2002.

[17]   C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998.

[18]   R. H. Byrd, P. Lu, and J. Nocedal. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific and Statistical Computing, 16(5):1190–1208, 1995.

[19]   R. H. B. C. Zhu and J. Nocedal. L-bfgs-b: Algorithm 778: L-bfgs-b, fortran routines for large scale bound constrained optimization. ACM Transactions on Mathematical Software, 23(4):550–560, November 1997.

[20]   B. cai Gao. NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3):257–266, Dec. 1996.

[21]   K. Castleman. Digital Image Processing. Prentice Hall, Upper Saddle River, NJ, 1996.

[22]   G. Celeux and J. Diebolt. The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational Statistics Quarterly, 2(1):73–82, 1985.

[23]   T.-H. Chan, C.-Y. Chi, Y.-M. Huang, and W.-K. Ma. A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. Signal Processing, IEEE Transactions on, 57(11):4418 –4432, nov. 2009.

[24]   E. Christophe and J. Inglada. Robust road extraction for high resolution satellite images. In IEEE International Conference on Image Processing, ICIP07, 2007.

[25]   A. Chung, W. Wells, A. Norbash, and W. Grimson. Multi-modal image registration by minimising kullback-leibler distance. In MICCAI’02 Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, pages 525–532, 2002.

[26]   J. Clevers. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25:53–69, 1988.

[27]   J. Clevers. Application of the wdvi in estimating lai at the generative stage of barley. ISPRS Journal of Photogrammetry and Remote Sensing, 46(1):37–47, 1991.

[28]   A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal. Automated multimodality image registration based on information theory. In Information Processing in Medical Imaging 1995, pages 263–274. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1995.

[29]   D. Commaniciu. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603–619, May 2002.

[30]   P. R. Coppin, I. Jonckheere, and K. Nachaerts. Digital change detection in ecosystem monitoring: a review. Int. J. of Remote Sensing, 24:1–33, 2003.

[31]   R. E. Crippen. Calculating the vegetation index faster. Remote Sensing of Environment, 34(1):71–73, 1990.

[32]   P. E. Danielsson. Euclidean distance mapping. Computer Graphics and Image Processing, 14:227–248, 1980.

[33]   M. H. Davis, A. Khotanzad, D. P. Flamig, and S. E. Harms. A physics-based coordinate transformation for 3-d image matching. IEEE Transactions on Medical Imaging, 16(3), June 1997.

[34]   P. Deer. Digital change detection in remotely sensed imagery using fuzzy set theory. Phd thesis, University of Adela¨i de, Australia, Department of Geography and Computer Science, 1998.

[35]   D. W. Deering, J. W. Rouse, R. H. Haas, and H. H. Schell. Measuring ¨f    orage productionöf grazing units from Landsat-MSS data. In Proceedings of the Tenth International Symposium on Remote Sensing of the Environment. ERIM, Ann Arbor, Michigan, USA, pages 1169–1198, 1975.

[36]   R. Deriche. Fast algorithms for low level vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):78–87, 1990.

[37]   R. Deriche. Recursively implementing the gaussian and its derivatives. Technical Report 1893, Unite de recherche INRIA Sophia-Antipolis, avril 1993. Research Repport.

[38]   S. Derrode, G. Mercier, and W. Pieczynski. Unsupervised change detection in SAR images using a multicomponent hidden Markov chain model. In Second Int. Workshop on the Analysis of Multi-temporal Remote Sensing Images, volume 3, pages 195–203, Ispra, Italy, July 16-18 2003.

[39]   A. Desolneux, L. Moisan, and J.-M. Morel. Meaningful alignments. Int. J. Comput. Vision, 40(1):7–23, 2000.

[40]   C. Dodson and T. Poston. Tensor Geometry: The Geometric Viewpoint and its Uses. Springer, 1997.

[41]   J. R. Dominique Fasbender and P. Bogaert. Bayesian data fusion for adaptable image pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 46(6):1847–1857, 2007.

[42]   S. Dudani, K. Breeding, and R. McGhee. Aircraft identification by moments invariants. IEEE Transanctions on Computers, 26:39–45, 1977.

[43]   V. N. Dvorchenko. Bounds on (deterministic) correlation functions with applications to registration. IEEE Trans. PAMI, 5(2):206–213, 1983.

[44]   D. Eberly. Ridges in Image and Data Analysis. Kluwer Academic Publishers, Dordrecht, 1996.

[45]   P. et al. Caracteristiques spectrales des surfaces sableuses de la region cotiere nord-ouest de l’Egypte: application aux donnees satellitaires Spot. In 2eme Journeees de Teledetection: Caracterisation et suivi des milieux terrestres en regions arides et tropicales, pages 27–38. ORSTOM, Collection Colloques et Seminaires, Paris, Dec. 1990.

[46]   J. Flusser. On the independence of rotation moment invariants. Pattern Recognition, 33:1405–1410, 2000.

[47]   I. Fodor. A survey of dimension reduction techniques. Technical report, 2002.

[48]   E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns, Elements of Reusable Object-Oriented Software. Professional Computing Series. Addison-Wesley, 1995.

[49]   G. Gerig, O. Kübler, R. Kikinis, and F. A. Jolesz. Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 11(2):221–232, June 1992.

[50]   R. Gonzalez and R. Woods. Digital Image Processing. Addison-Wesley, Reading, MA, 1993.

[51]   H. Gray. Gray’s Anatomy. Merchant Book Company, sixteenth edition, 2003.

[52]   A. Green, M. Berman, P. Switzer, and M. Craig. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. Geoscience and Remote Sensing, IEEE Transactions on, 26(1):65–74, 1988.

[53]   S. Grossberg. Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance. Perception and Psychophysics, 36(5):428–456, 1984.

[54]   J. Hajnal, D. J. Hawkes, and D. Hill. Medical Image Registration. CRC Press, 2001.

[55]   W. R. Hamilton. Elements of Quaternions. Chelsea Publishing Company, 1969.

[56]   R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6):610–621, Nov 1973.

[57]   D. Heinz and Chein-I-Chang. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. Geoscience and Remote Sensing, IEEE Transactions on, 39(3):529 –545, mar 2001.

[58]   M. Holden, D. L. G. Hill, E. R. E. Denton, J. M. Jarosz, T. C. S. Cox, and D. J. Hawkes. Voxel similarity measures for 3d serial mr brain image registration. In A. Kuba, M. Samal, and A. Todd-Pkropek, editors, Information Processing in Medical Imaging 1999 (IPMI’99), pages 472–477. Springer, 1999.

[59]   C. Hsu and C. Lin. A comparison of methods for multi-class support vector machines, 2001.

[60]   M. K. Hu. Visual Pattern Recognition by moment invariants. IEEE Transactions on Information Theory, 8(2):179–187, 1962.

[61]   X. Huang, L. Zhang, and P. Li. Classification and extraction of spatial features in urban areas using high-resolution multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 4(2):260–264, Apr. 2007.

[62]   A. Huck. Analyse non-supervisée d’images hyperspectrales: démixage linéaire et détection d’anomalies. PhD thesis, Université de Marseille, 2009.

[63]   A. Huck and M. Guillaume. Robust hyperspectral data unmixing with spatial and spectral regularized nmf. In Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010 2nd Workshop on, pages 1 –4, june 2010.

[64]   A. R. Huete. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25:295–309, 1988.

[65]   A. R. Huete, C. Justice, and H. Liu. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment, 49:224–234, 1994.

[66]   A. Hyvarinen. Fast and robust fixed-point algorithms for independent component analysis. Neural Networks, IEEE Transactions on, 10(3):626–634, 1999.

[67]   C. Igel, V. Heidrich-Meisner, and T. Glasmachers. Shark. Journal of Machine Learning Research, 9:993–996, 2008.

[68]   J. Inglada. Similarity Measures for Multisensor Remote Sensing Images. In International Geoscience and Remote Sensing Symposium, IGARSS 2002, CD-ROM, 2002.

[69]   J. Inglada. Change detection on SAR images by using a parametric estimation of the Kullback-Leibler divergence. In IEEE Int. Conf. Geosci. Remote Sensing, Toulouse, France, July, 21-25 2003.

[70]   J. Inglada and A. Giros. On the possibility of automatic multi-sensor image registration. IEEE Trans. Geoscience and Remote Sensing, 42(10), Oct. 2004.

[71]   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 Trans. Geosci. Remote Sensing, 45(5):1432–1446, May 2007.

[72]   S. Jacquemoud, W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. François, and S. L. Ustin. Prospect + sail models: A review of use for vegetation characterization. Remote Sensing of Environment, 113(Supplement 1):S56 – S66, 2009. Imaging Spectroscopy Special Issue.

[73]   J.Flusser and T. Suk. A moment based approach to registration of image with affine geometric distortion. IEEE Transactions Geoscience Remote Sensing, 32(2):382–387, 1994.

[74]   T. Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Technical report, Computer Science of The University of dortmund, Nov. 1997.

[75]   C. J. Joly. A Manual of Quaternions. MacMillan and Co. Limited, 1905.

[76]   C. O. Justice, E. Vermote, J. R. G. Townshend, R. Defries, D. P. Roy, D. K. Hall, V. V. Salomonson, J. L. Privette, G. Riggs, A. Strahler, W. Lucht, R. B. Myneni, Y. Knyazikhin, S. W. Running, R. R. Nemani, Z. Wan, A. R. Huete, W. van Leeuwen, R. E. Wolfe, L. Giglio, J.-P. Muller, P. Lewis, , and M. J. Barnsley. The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36:1–22, 1998.

[77]   C. Jutten and J. Herault. Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. Signal processing, 24(1):1–10, 1991.

[78]   T. Kanungo, D. M. Mount, N. S. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation.

[79]   Y. J. Kaufman and D. Tanré. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. Transactions on Geoscience and Remote Sensing, 40(2):261–270, Mar. 1992.

[80]   J. Koënderink and A. van Doorn. The Structure of Two-Dimensional Scalar Fields with Applications to Vision. Biol. Cybernetics, 33:151–158, 1979.

[81]   J. Koenderink and A. van Doorn. Local features of smooth shapes: Ridges and courses. SPIE Proc. Geometric Methods in Computer Vision II, 2031:2–13, 1993.

[82]   C. Kuglin and D. Hines. The phase correlation image alignment method. In IEEE Conference on Cybernetics and Society, pages 163–165, 1975.

[83]   J. Lacauxa, Y. T. andC. Vignollesa, J. Ndioneb, and M. Lafayec. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley fever epidemics in Senegal. Remote Sensing of Environment, 106(1):66–74, 2007.

[84]   V. Lacroix and M. Acheroy. Feature extraction using the constrained gradient. ISPRS Journal of Photogrammetry & Remote Sensing, 53:85–94, 1998.

[85]   J. Lee. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2:165–168, 1980.

[86]   J. Lee, A. Woodyatt, and M. Berman. Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform. Geoscience and Remote Sensing, IEEE Transactions on, 28(3):295–304, 1990.

[87]   J. Li and J. Bioucas-Dias. Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data. In Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, volume 3, pages III –250 –III –253, july 2008.

[88]   T. Lindeberg. Scale-Space Theory in Computer Science. Kluwer Academic Publishers, 1994.

[89]   H. Lodish, A. Berk, S. Zipursky, P. Matsudaira, D. Baltimore, and J. Darnell. Molecular Cell Biology. W. H. Freeman and Company, 2000.

[90]   D. Lu, P. Mausel, E. Brondizio, and E. Moran. Change detection techniques. Int. J. of Remote Sensing, 25(12):2365–2407, 2004.

[91]   F. Maes, A. Collignon, D. Meulen, G. Marchal, and P. Suetens. Multi-modality image registration by maximization of mutual information. IEEE Trans. on Med. Imaging, 16:187–198, 1997.

[92]   D. Malacara. Color Vision and Colorimetry: Theory and Applications. SPIE PRESS, 2002.

[93]   D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank. Non-rigid multimodality image registration. In Medical Imaging 2001: Image Processing, pages 1609–1620, 2001.

[94]   D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank. PET-CT image registration in the chest using free-form deformations. IEEE Trans. on Medical Imaging, 22(1):120–128, Jan. 2003.

[95]   S. K. McFeeters. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7):1425–1432, 1996.

[96]   D. Musser and A. Saini. STL Tutorial and Reference Guide. Professional Computing Series. Addison-Wesley, 1996.

[97]   J. Nascimento and J. Dias. Vertex component analysis: a fast algorithm to unmix hyperspectral data. Geoscience and Remote Sensing, IEEE Transactions on, 43(4):898 – 910, april 2005.

[98]   Y. Z. J. G. S. Ni. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3):583–594, 2003.

[99]   E. Nicoloyanni. Un indice de changement diachronique appliqué deux sècnes Landsat MSS sur Athènes (grèce). International Journal of Remote Sensing, 11(9):1617–1623, 1990.

[100]   A. Nielsen. The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data. Image Processing, IEEE Transactions on, 16(2):463–478, 2007.

[101]   A. Nielsen. Kernel maximum autocorrelation factor and minimum noise fraction transformations. Image Processing, IEEE Transactions on, (99):1–1, 2011.

[102]   V. Onana, E. Trouvé, G. Mauris, J. Rudant, and P. Frison. Change detection in urban context with multitemporal ERS-SAR images by using data fusion approach. In IEEE Int. Conf. Geosci. Remote Sensing, Toulouse, France, July, 21-25 2003.

[103]   E. Osuna, R. Freund, and F. Girosi. Training support vector machines:an application to face detection, 1997.

[104]   R. L. Pearson and L. D. Miller. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, pawnee national grasslands, colorado. In Proceedings of the 8th International Symposium on Remote Sensing of the Environment II, pages 1355–1379, 1972.

[105]   D. Pelleg and A. Moore. Accelerating exact k -means algorithms with geometric reasoning. In Fifth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pages 277–281, 1999.

[106]   G. P. Penney, J. Weese, J. A. Little, P. Desmedt, D. L. G. Hill, and D. J. Hawkes. A comparision of similarity measures for use in 2d-3d medical image registration. IEEE Transactions on Medical Imaging, 17(4):586–595, August 1998.

[107]   P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis Machine Intelligence, 12:629–639, 1990.

[108]   M. Pesaresi, A. Gerhardinger, and F. Kayitakire. A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(3):180–192, Sept. 2008.

[109]   B. Pinty and M. M. Verstraete. GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101:15–20, 1992.

[110]   J. P. Pluim, J. B. A. Maintz, and M. A. Viergever. Mutual-Information-Based Registration of Medical Images: A Survey. IEEE Transactions on Medical Imaging, 22(8):986–1004, Aug. 2003.

[111]   S. Plummer, P. North, and S. Briggs. The Angular Vegetation Index (AVI): an atmospherically resistant index for the Second Along-Track Scanning Radiometer (ATSR-2). In Sixth International Symposium on Physical Measurements and Spectral Signatures in Remote Sensing, Val d’Isere, 1994.

[112]   W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C. Cambridge University Press, second edition, 1992.

[113]   J. Qi, A. . Chehbouni, A. Huete, Y. Kerr, and S. Sorooshian. A modified soil adjusted vegetation index. Remote Sensing of Environment, 47:1–25, 1994.

[114]   R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysman. Image change detection algorithms: a systematic survey. IEEE Trans. Image Processing, 14(3):294–307, March 2005.

[115]   I. Reed and X. Yu. Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution. Acoustics, Speech and Signal Processing, IEEE Transactions on, 38(10):1760 –1770, oct 1990.

[116]   J. A. Richards. Analysis of remotely sensed data: the formative decades and the fututre. IEEE Trans. Geoscience and Remote Sensing, 43(3):422–432, 2005.

[117]   A. J. Richardson and C. L. Wiegand. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12):1541–1552, 1977.

[118]   K. Rohr, M. Fornefett, and H. S. Stiehl. Approximating thin-plate splines for elastric registration: Integration of landmark errors and orientation attributes. In A. Kuba, M. Samal, and A. Todd-Pkropek, editors, Information Processing in Medical Imaging 1999 (IPMI’99), pages 252–265. Springer, 1999.

[119]   K. Rohr, H. S. Stiehl, R. Sprengel, T. M. Buzug, J. Weese, and M. H. Kuhn. Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on Medical Imaging, 20(6):526–534, June 1997.

[120]   J. W. Rouse. Monitoring the vernal advancement and retrogradation of natural vegetation. Type ii report, NASA/GSFCT, Greenbelt, MD, USA, 1973.

[121]   D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hill, M. O. Leach, and D. J. Hawkes. Nonrigid registration using free-form deformations: Application to breast mr images. IEEE Transaction on Medical Imaging, 18(8):712–721, 1999.

[122]   G. Sapiro and D. Ringach. Anisotropic diffusion of multivalued images with applications to color filtering. IEEE Trans. on Image Processing, 5:1582–1586, 1996.

[123]   S. Schweizer and J. Moura. Efficient detection in hyperspectral imagery. Image Processing, IEEE Transactions on, 10(4):584 –597, apr 2001.

[124]   J. P. Serra. Image Analysis and Mathematical Morphology. Academic Press Inc., 1982.

[125]   J. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press, 1996.

[126]   J. C. Spall. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest, 19:482–492, 1998.

[127]   E. Stabel and P. Fischer. Detection of structural changes in river dynamics by radar-based earth observation methods. In Proc. of the 1st Biennial Meeting of the Int. Environmental Modelling and Software Society, volume 1, pages 352–358, Lugano, Switzerland, June 2002.

[128]   M. Styner, C. Brehbuhler, G. Szekely, and G. Gerig. Parametric estimate of intensity homogeneities applied to MRI. IEEE Trans. Medical Imaging, 19(3):153–165, Mar. 2000.

[129]   B. M. ter Haar Romeny, editor. Geometry-Driven Diffusion in Computer Vision. Kluwer Academic Publishers, 1994.

[130]   R. Touzi, A. Lopes, and P. Bousquet. A statistical and geometrical edge detector for SAR images. IEEE Trans. Geoscience and Remote Sensing, 26(6):764–773, November 1988.

[131]   J. Townshend, C. Justice, C. Gurney, and J. McManus. The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 30(5):1054–1060, sept 1992.

[132]   F. Tupin, H. Maître, J.-F. Mangin, J.-M. Nicolas, and E. Pechersky. Detection of linear features in SAR images: application to road network extraction. IEEE Transactions on Geoscience and Remote Sensing, 36(2):434–453, Mar. 1998.

[133]   V. Vapnik. Statistical learning theory. John Wiley and Sons, NewYork, 1998.

[134]   E. F. Vermote, D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcette. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview. Geoscience and Remote Sensing, IEEE Transactions on, 35(3):675–686, 1997.

[135]   P. Viola and W. M. Wells III. Alignment by maximization of mutual information. IJCV, 24(2):137–154, 1997.

[136]   J. Weickert, B. ter Haar Romeny, and M. Viergever. Conservative image transformations with restoration and scale-space properties. In Proc. 1996 IEEE International Conference on Image Processing (ICIP-96, Lausanne, Sept. 16-19, 1996), pages 465–468, 1996.

[137]   J. Weston and C. Watkins. Multi-class support vector machines, 1998.

[138]   R. T. Whitaker. Characterizing first and second order patches using geometry-limited diffusion. In Information Processing in Medical Imaging 1993 (IPMI’93), pages 149–167, 1993.

[139]   R. T. Whitaker. Geometry-Limited Diffusion. PhD thesis, The University of North Carolina, Chapel Hill, North Carolina 27599-3175, 1993.

[140]   R. T. Whitaker. Geometry-limited diffusion in the characterization of geometric patches in images. Computer Vision, Graphics, and Image Processing: Image Understanding, 57(1):111–120, January 1993.

[141]   R. T. Whitaker and G. Gerig. Vector-Valued Diffusion, pages 93–134. Kluwer Academic Publishers, 1994.

[142]   R. T. Whitaker and S. M. Pizer. Geometry-based image segmentation using anisotropic diffusion. In Y.-L. O, A. Toet, H. Heijmans, D. Foster, and P. Meer, editors, Shape in Picture: The mathematical description of shape in greylevel images. Springer Verlag, Heidelberg, 1993.

[143]   R. T. Whitaker and S. M. Pizer. A multi-scale approach to nonuniform diffusion. Computer Vision, Graphics, and Image Processing: Image Understanding, 57(1):99–110, January 1993.

[144]   R. T. Whitaker and X. Xue. Variable-Conductance, Level-Set Curvature for Image Processing. In International Conference on Image Processing, pages 142–145. IEEE, 2001.

[145]   C. L. Wiegand, A. J. Richardson, D. E. Escobar, and A. H. Gerbermann. Vegetation indices in crop assessments. Remote Sensing of Environment, 35:105–119, 1991.

[146]   G. Wyszecki. Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley-Interscience, 2000.

[147]   H. Xu. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14):3025–3033, 2006.

[148]   T. Yoo, U. Neumann, H. Fuchs, S. Pizer, T. Cullip, J. Rhoades, and R. Whitaker. Direct visualization of volume data. IEEE Computer Graphics and Applications, 12(4):63–71, 1992.

[149]   T. Yoo, S. Pizer, H. Fuchs, T. Cullip, J. Rhoades, and R. Whitaker. Achieving direct volume visualization with interactive semantic region selection. In Information Processing in Medical Images. Springer Verlag, 1991.

[150]   T. S. Yoo and J. M. Coggins. Using statistical pattern recognition techniques to control variable conductance diffusion. In Information Processing in Medical Imaging 1993 (IPMI’93), pages 459–471, 1993.

[151]   P. J. Zarco-Tejada and S. Ustin. Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites. In International Geoscience and Remote Sensing Symposium, IGARSS ’01, pages 342–344, 2001.