Local scale measure from the topographic map and application to remote sensing images

Multiscale Modeling and Simulation

Volumn 8, Issue 1, 2009, Pages 1-29

  Luo, Bin ^a,b   Aujol, Jean Francois ^c   Gousseau, Yann ^d  

^a TELECOM PARISTECH   (France)

^b GIPSA LAB   (France)

^c UNIVERSITÉ PARIS SACLAY   (France)

^d INSTITUT TELECOM   (France)

Author keywords

Local scale; Remote sensing images; Topographic map

Indexed keywords

COMPLEX PARAMETER; CONNECTED COMPONENT; HIERARCHICAL STRUCTURES; LEVEL LINES; LINEAR SCALE; LOCAL SCALE; MORPHOLOGICAL APPROACH; REMOTE SENSING IMAGES; SATELLITE IMAGES; TOPOGRAPHIC MAP; TOTAL VARIATION FLOW; VARIATIONAL APPROACHES;

IMAGE RECONSTRUCTION; REMOTE SENSING; SPACE OPTICS;

MAPS;

EID: 70350281282     PISSN: 15403459     EISSN: 15403467     Source Type: Journal    
DOI: 10.1137/080730627     Document Type: Article

References (52)

1. F. ALTER AND V. CASELLES, Uniqueness of the Cheeger set of a convex body, Nonlinear Anal., 70 (2009), pp. 32-44.
2. L. AMBROSIO, N. FUSCO, AND D. PALLARA, Functions of Bounded Variations and Free Discontinuity Problems, Oxford Math. Monogr., The Clarendon Press, Oxford University Press, New York, 2000.
3. n, J. Differential Equations, 184 (2002), pp. 475-525.
4. 2, SIAM J. Math. Anal., 36 (2005), pp. 1095-1129.
5. J. A. BENEDIKTSSON, M. PESARESI, AND K. ARNASON, Classification and feature extraction for remote sensing images from urban areas based on morphological transformations, IEEE Trans. Geosci. Remote Sens., 41 (2003), pp. 1940-1949.
6. J. BESAG, On the statistical analysis of dirty pictures, J. Roy. Statist. Soc. Ser. B, 48 (1986), pp. 259-302.
7. T. BROX AND J. WEICKERT, A TV flow based local scale measure for texture discrimination, in Computer Vision - ECCV 2004, Vol. 2, Springer, Berlin, 2004, pp. 578-590.
8. T. BROX AND J. WEICKERT, A TV flow based local scale estimate and its application to texture discrimination, J. Visual Communication and Image Representation, 17 (2006), pp. 1053-1073.
9. F. CAO, J.-L. LISANI, J.-M. MOREL, P. MUSÉ, AND F. SUR, A Theory of Shape Identification, Lecture Notes in Math. 1948, Springer, Berlin, 2008.
10. V. CASELLES, A. CHAMBOLLE, AND M. NOVAGA, The discontinuity set of solutions of the TV denoising problem and some extensions, Multiscale Model. Simul., 6 (2007), pp. 879-894.
11. V. CASELLES, B. COLL, AND J. M. MOREL, Scale space versus topographic map for natural images, in Proceedings of the First International Conference on Scale-Space Theory in Computer Vision, Springer, London, 1997, pp. 29-49.
12. V. CASELLES, B. COLL, AND J.-M. MOREL, Topographic maps and local contrast changes in natural images, Int. J. Comput. Vision, 33 (1999), pp. 5-27.
13. J. CHANUSSOT, J. A. BENEDIKTSSON, AND M. FAUVEL, Classification of remote sensing images from urban areas using a fuzzy possibilistic model, IEEE Geosci. Remote Sens. Lett., 3 (2006), pp. 40-44.
14. A. DESOLNEUX, L. MOISAN, AND J. M. MOREL, Edge detection by Helmholtz principle, J. Math. Imaging Vision, 14 (2001), pp. 271-284.
15. F. DIBOS AND G. KOEPFLER, Total variation minimization by the fast level sets transform, in Proceedings of the IEEE Workshop on Variational and Level Set Methods in Computer Vision, 2001, pp. 179-185.
16. F. DIBOS, G. KOEPFLER, AND P. MONASSE, Total variation minimization for scalar/vector regularization, in Geometric Level Set Methods in Imaging, Vision, and Graphics, S. Osher and N. Paragios, eds., Springer, New York, 2003, pp. 121-140.
17. E. W. FORGY, Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications, Biometrics, 21 (1965), pp. 768-769.
18. A. HAAS, G. MATHERON, AND J. SERRA, Morphologic mathématique et granulométries en place, Annales des Mines, 11 (1967), pp. 736-753.
19. H. J. A. M. HEIJMANS, Connected morphological operators for binary images, Comput. Vis. Image Underst., 73 (1999), pp. 99-120.
20. M. JÄGERSTAND, Saliency maps and attention selection in scale and spatial coordinates: An information theoretic approach, in Proceedings of the 5th International IEEE Conference on Computer Vision, Cambridge, MA, 1995, pp. 195-202.
21. T. KADIR AND M. BRADY, Scale, saliency and image description, Int. J. Comput. Vision, 45 (2001), pp. 83-105.
22. T. KADIR, A. ZISSERMAN, AND M. BRADY, An affine invariant salient region detector, in Proceedings of the European Conference on Computer Vision, 2004.
23. S. LADJAL, Flou et quantification dans les images numériques, Ph.D. thesis, ENS de Cachan, Cachan, France, 2005.
24. C. LATRY AND B. ROUGE, SPOT5 THR mode, in Proceedings of the SPIE Earth Observing Systems III, Vol. 3493, 1998, pp. 480-491.
25. T. LINDEBERG, Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of-attention, Int. J. Comput. Vision, 11 (1993), pp. 283-318.
26. T. LINDEBERG, Feature detection with automatic scale selection, Int. J. Comput. Vision, 30 (1998), pp. 79-116. (Pubitemid 128517917)
27. A. LORETTE, Analyse de texture par méthodes markoviennes et par morphologic mathématique: application à l'analyse des zones urbaines sur des images satellitales, Ph.D. thesis, Université de Nice Sophia Antipolis, Nice, France, 1999.
28. D. G. LOWE, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision, 60 (2004), pp. 91-110.
29. B. LUO, Echelle et résolution en imagerie de télé détection, Ph.D. thesis, Télécom ParisTech, Paris, France, 2007.
30. B. LUO, J.-F. AUJOL, Y. GOUSSEAU, S. LADJAL, AND H. MAÎTRE, Resolution independent characteristic scale dedicated to satellite images, IEEE Trans. Image Process., 16 (2007), pp. 2503-2514.
31. P. MARAGOS, Pattern spectrum and multiscale shape representation, IEEE Trans. Pattern Anal. Mach. Intell., 11 (1989), pp. 701-716.
32. S. MASNOU AND J.-M. MOREL, Image restoration involving connectedness, in Proceedings of the 6th SPIE International Workshop on Digital Image Processing and Computer Graphics, 1997, pp. 84-95.
33. J. MATAS, O. CHUM, U. MARTIN, AND T. PAJDLA, Robust wide baseline stereo from maximally stable extremal regions, in Proceedings of the British Machine Vision Conference, Vol. 1, British Machine Vision Association, Malvern, Worcestershire, UK, 2002, pp. 384-393.
34. Y. MEYER, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures, AMS, Providence, RI, 2001.
35. K. MIKOLAJCZYK AND C. SCHMID, Scale and affine invariant interest point detectors, Int. J. Comput. Vision, 60 (2004), pp. 63-86.
36. P. MONASSE, Morphological Representation of Digital Images and Application to Registration, Ph.D. thesis, University Paris IX, Paris, France, 2000.
37. P. MONASSE AND F. GUICHARD, Scale-space from a level lines tree, in Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision, Springer, Berlin, 1999, pp. 175-186.
38. P. MONASSE AND F. GUICHARD, Fast computation of a contrast-invariant image representation, IEEE Trans. Image Process., 9 (2000), pp. 860-872.
39. D. R. MONTELLO, Scale in Geography, Pergamon Press, Oxford, UK, 2001.
40. NATO, STANAG 3769: Minimum Resolved Object Sizes and Scales for Imagery Interpretation, 1998.
41. L. RUDIN, S. OSHER, AND E. FATEMI, Nonlinear total variation based noise removal algorithms, Phys. D, 60 (1992), pp. 259-268.
42. P. SALEMBIER AND J. SERRA, Flat zones filtering, connected operators, and filters by reconstruction, IEEE Trans. Image Process., 4 (1995), pp. 1153-1160.
43. J. SERRA, Image Analysis and Mathematical Morphology, Vol. 2, Academic Press, London, 1988.
44. P. SOILLE AND M. PESARESI, Advances in mathematical morphology applied to geoscience and remote sensing, IEEE Trans. Geosci. Remote Sens., 40 (2002), pp. 2042-2055.
45. Y. SONG, A topdown algorithm for computation of level line trees, IEEE Trans. Image Process., 16 (2007), pp. 2107-2116.
46. J. SPORRING, The entropy of scale-space, in Proceedings of the 13th International IEEE Conference on Pattern Recognition, Vol. A, 1996, pp. 900-904.
47. J. SPORRING AND J. WEICKERT, On generalized entropies and scale-space, in Proceedings of the First International Conference on Scale-Space Theories in Computer Vision, Springer, Berlin, 1997, pp. 53-64.
48. G. STEIDL, J. WEICKERT, T. BROX, P. MRÁZEK, AND M. WELK, On the equivalence of soft wavelet shrinkage, total variation diffusion, total variation regularization, and SIDEs, SIAM J. Numer. Anal., 42 (2004), pp. 686-713.
49. D. STOYAN, W. S. KENDALL, AND J. MECKE, Stochastic Geometry and Its Applications, 2nd ed., Wiley, Chichester, 1995.
50. D. M. STRONG, J.-F. AUJOL, AND T. F. CHAN, Scale recognition, regularization parameter selection, and Meyer's G norm in total variation regularization, Multiscale Model. Simul., 5 (2006), pp. 273-303.
51. D. STRONG AND T. CHAN, Edge-preserving and scale-dependent properties of total variation regularization, Inverse Problems, 19 (2003), pp. 165-187.
52. A. WINTER, H. MAÎTRE, N. CAMBOU, AND E. LEGRAND, An original multi-sensor approach to scale-based image analysis for aerial and satellite images, in Proceedings of the 1997 International IEEE Conference on Image Processing, Vol. II, Santa Barbara, CA, 1997, pp. 234-237.