Abstract

We present a new nonparametric cooperative approach to multiband image segmentation. It is based on cooperation between region-growing segmentation and edge segmentation. This approach requires no input data other than the images to be processed. It uses a spectral homogeneity criterion whose threshold is determined automatically. The threshold is adaptive and varies depending on the objects to be segmented. Applying this new approach to very high resolution satellite imagery has yielded satisfactory results. The approach demonstrated its performance on images of varied complexity and was able to detect objects of great spatial and spectral heterogeneity.

© 2009 Optical Society of America

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  1. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recogn. 26, 1277-1294 (1993).
    [CrossRef]
  2. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).
  3. M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
    [CrossRef]
  4. C. D. Kermad and K. Chehdi, “Segmentation d'images : recherche d'une mise en oeuvre automatique par coopération de méthodes,” Trait. Signal 15, 331-336 (1998).
  5. W. Skarbek and A. Koschan, “Colour Image Segmentation: A Survey,” technical report, Technical University of Berlin, 1994.
  6. Y.-J. Zhang, “An overview of image and video segmentation in the last 40 years,” in Advances in Image and Video Segmentation, Y.-J.Zhang, ed. (IRM Press, 2006), pp. 1-15.
    [CrossRef]
  7. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications (Springer-Verlag, 2000).
  8. H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
    [CrossRef]
  9. L. Lucchese and S. K. Mitra, “Color image segmentation: a state-of-the-art survey,” in Proceedings of the Indian National Science Academy (INSA-A) (New Delhi, India, 2001), pp. 207-221.
  10. F. Bellet, M. Salotti, and C. Garbay, “Une approche opportuniste et coopérative pour la vision de bas niveau,” Trait. Signal 12, 479-494 (1995).
  11. J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
    [CrossRef]
  12. R. Fjørtoft, “Segmentation d'images radar par détection de contours,” Ph.D. thesis (Institut National Polytechnique de Toulouse, 1999).
  13. X. Cufi, X. Muñoz, J. Freixenet, and J. Marti, “A review on image segmentation techniques integrating region and boundary information,” in Advances in Imaging and Electron Physics, P.W.Hawkes, ed. (Academic Press, 2001), pp. 1-50.
  14. X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
    [CrossRef]
  15. I. Sebari and D-C. He, “Les approches de segmentation d'image par coopération régions-contours,” Télédétection 7, 499-506 (2007).
  16. M. Mueller, K. Segl, and H. Kaufmann, “Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,” Pattern Recogn. 37, 1619-1628 (2004).
    [CrossRef]
  17. X. Muñoz, X. Cufí, J. Freixenet, and J. Martí, “A New approach to segmentation based on fusing circumscribed contours, region growing and clustering,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2000), pp. 800-803.
  18. C. Chu and J. Aggarwal, “The integration of image segmentation maps using region and edge information,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1241-1252 (1993).
    [CrossRef]
  19. D. Zugaj and V. Lattuati, “A new approach of color images segmentation based on fusing region and edge segmentations outputs,” Pattern Recogn. 31, 105-113 (1998).
    [CrossRef]
  20. J. M. Salotti, “Gestion des informations dans les premières étapes de la vision par ordinateur,” Ph.D. thesis (Institut National Polytechnique de Grenoble, 1994).
  21. S. A. Barker and P. J. W. Rayner, “Unsupervised image segmentation using Markov random field models,” Pattern Recogn. 33, 587-602 (2000).
    [CrossRef]
  22. C.-T. Li, “Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm,” Signal Process. 83, 67-78 (2003).
    [CrossRef]
  23. R. Bajcsy, S. W. Lee, and A. Leonardis, “Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation,” Int. J. Comput. Vis. 17, 241-272(1996).
    [CrossRef]
  24. A. Moghaddamzadeh and N. Bourbakis, “A fuzzy region growing approach for segmentation of color images,” Pattern Recogn. 30, 867-881 (1997).
    [CrossRef]
  25. P. Bertolino, “Contribution des pyramides irrégulières en segmentation d'images multirésolution,” Ph.D. thesis (Institut National Polytechnique de Grenoble, 1995).
  26. A. M. Nazif and M. D. Levine, “Low level image segmentation: an expert system,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 555-577 (1984).
    [CrossRef]
  27. R. Caloz and A. Pointet, “Analyse comparative de la classification contextuelle et du maximum de vraisemblance: synthèse et cas d'étude,” Télédétection 3, 311-322(2003).
  28. R. Caloz and C. Collet, Précis de Télédétection--Vol. 3: Traitements Numériques d'Images de Télédétection (Université du Québec/AUF, 2001).
  29. M. Nagao and T. Matsuyama, A Structural Analysis of Complex Aerial Photographs (Plenum, 1980).
  30. G. Zack, W. Rogers, and S. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem. 25, 741-753 (1977).
    [CrossRef] [PubMed]
  31. M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Comput. Graph. Image Process. 9, 394-407 (1979).
    [CrossRef]
  32. D.-C. He, L. Wang, and M. Amani, “A new technique for multi-resolution image fusion,” in Proceedings of IGARSS-International Geoscience and Remote Sensing Symposium (IEEE, 2004), pp. 19-26.
  33. A. P. Carleer, O. Debeir, and E. Wolff, “Assessment of very high spatial resolution satellite image segmentations,” Photogramm. Eng. Remote Sensing 71, 1285-1294 (2005).

2007 (1)

I. Sebari and D-C. He, “Les approches de segmentation d'image par coopération régions-contours,” Télédétection 7, 499-506 (2007).

2005 (1)

A. P. Carleer, O. Debeir, and E. Wolff, “Assessment of very high spatial resolution satellite image segmentations,” Photogramm. Eng. Remote Sensing 71, 1285-1294 (2005).

2004 (1)

M. Mueller, K. Segl, and H. Kaufmann, “Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,” Pattern Recogn. 37, 1619-1628 (2004).
[CrossRef]

2003 (4)

M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
[CrossRef]

X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
[CrossRef]

C.-T. Li, “Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm,” Signal Process. 83, 67-78 (2003).
[CrossRef]

R. Caloz and A. Pointet, “Analyse comparative de la classification contextuelle et du maximum de vraisemblance: synthèse et cas d'étude,” Télédétection 3, 311-322(2003).

2001 (2)

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
[CrossRef]

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

2000 (1)

S. A. Barker and P. J. W. Rayner, “Unsupervised image segmentation using Markov random field models,” Pattern Recogn. 33, 587-602 (2000).
[CrossRef]

1998 (2)

C. D. Kermad and K. Chehdi, “Segmentation d'images : recherche d'une mise en oeuvre automatique par coopération de méthodes,” Trait. Signal 15, 331-336 (1998).

D. Zugaj and V. Lattuati, “A new approach of color images segmentation based on fusing region and edge segmentations outputs,” Pattern Recogn. 31, 105-113 (1998).
[CrossRef]

1997 (1)

A. Moghaddamzadeh and N. Bourbakis, “A fuzzy region growing approach for segmentation of color images,” Pattern Recogn. 30, 867-881 (1997).
[CrossRef]

1996 (1)

R. Bajcsy, S. W. Lee, and A. Leonardis, “Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation,” Int. J. Comput. Vis. 17, 241-272(1996).
[CrossRef]

1995 (1)

F. Bellet, M. Salotti, and C. Garbay, “Une approche opportuniste et coopérative pour la vision de bas niveau,” Trait. Signal 12, 479-494 (1995).

1993 (2)

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recogn. 26, 1277-1294 (1993).
[CrossRef]

C. Chu and J. Aggarwal, “The integration of image segmentation maps using region and edge information,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1241-1252 (1993).
[CrossRef]

1984 (1)

A. M. Nazif and M. D. Levine, “Low level image segmentation: an expert system,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 555-577 (1984).
[CrossRef]

1979 (1)

M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Comput. Graph. Image Process. 9, 394-407 (1979).
[CrossRef]

1977 (1)

G. Zack, W. Rogers, and S. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem. 25, 741-753 (1977).
[CrossRef] [PubMed]

Aggarwal, J.

C. Chu and J. Aggarwal, “The integration of image segmentation maps using region and edge information,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1241-1252 (1993).
[CrossRef]

Amani, M.

D.-C. He, L. Wang, and M. Amani, “A new technique for multi-resolution image fusion,” in Proceedings of IGARSS-International Geoscience and Remote Sensing Symposium (IEEE, 2004), pp. 19-26.

Aref, W. G.

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

Bajcsy, R.

R. Bajcsy, S. W. Lee, and A. Leonardis, “Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation,” Int. J. Comput. Vis. 17, 241-272(1996).
[CrossRef]

Barker, S. A.

S. A. Barker and P. J. W. Rayner, “Unsupervised image segmentation using Markov random field models,” Pattern Recogn. 33, 587-602 (2000).
[CrossRef]

Bellet, F.

F. Bellet, M. Salotti, and C. Garbay, “Une approche opportuniste et coopérative pour la vision de bas niveau,” Trait. Signal 12, 479-494 (1995).

Bertolino, P.

P. Bertolino, “Contribution des pyramides irrégulières en segmentation d'images multirésolution,” Ph.D. thesis (Institut National Polytechnique de Grenoble, 1995).

Bourbakis, N.

A. Moghaddamzadeh and N. Bourbakis, “A fuzzy region growing approach for segmentation of color images,” Pattern Recogn. 30, 867-881 (1997).
[CrossRef]

Caloz, R.

R. Caloz and A. Pointet, “Analyse comparative de la classification contextuelle et du maximum de vraisemblance: synthèse et cas d'étude,” Télédétection 3, 311-322(2003).

R. Caloz and C. Collet, Précis de Télédétection--Vol. 3: Traitements Numériques d'Images de Télédétection (Université du Québec/AUF, 2001).

Carleer, A. P.

A. P. Carleer, O. Debeir, and E. Wolff, “Assessment of very high spatial resolution satellite image segmentations,” Photogramm. Eng. Remote Sensing 71, 1285-1294 (2005).

Chehdi, K.

C. D. Kermad and K. Chehdi, “Segmentation d'images : recherche d'une mise en oeuvre automatique par coopération de méthodes,” Trait. Signal 15, 331-336 (1998).

Cheng, H. D.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
[CrossRef]

Chu, C.

C. Chu and J. Aggarwal, “The integration of image segmentation maps using region and edge information,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1241-1252 (1993).
[CrossRef]

Collet, C.

R. Caloz and C. Collet, Précis de Télédétection--Vol. 3: Traitements Numériques d'Images de Télédétection (Université du Québec/AUF, 2001).

Cufi, X.

X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
[CrossRef]

X. Cufi, X. Muñoz, J. Freixenet, and J. Marti, “A review on image segmentation techniques integrating region and boundary information,” in Advances in Imaging and Electron Physics, P.W.Hawkes, ed. (Academic Press, 2001), pp. 1-50.

Cufí, X.

X. Muñoz, X. Cufí, J. Freixenet, and J. Martí, “A New approach to segmentation based on fusing circumscribed contours, region growing and clustering,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2000), pp. 800-803.

Debeir, O.

A. P. Carleer, O. Debeir, and E. Wolff, “Assessment of very high spatial resolution satellite image segmentations,” Photogramm. Eng. Remote Sensing 71, 1285-1294 (2005).

Elmagarmid, A. K.

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

Fan, J.

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

Fjørtoft, R.

R. Fjørtoft, “Segmentation d'images radar par détection de contours,” Ph.D. thesis (Institut National Polytechnique de Toulouse, 1999).

Freixenet, J.

X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
[CrossRef]

X. Muñoz, X. Cufí, J. Freixenet, and J. Martí, “A New approach to segmentation based on fusing circumscribed contours, region growing and clustering,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2000), pp. 800-803.

X. Cufi, X. Muñoz, J. Freixenet, and J. Marti, “A review on image segmentation techniques integrating region and boundary information,” in Advances in Imaging and Electron Physics, P.W.Hawkes, ed. (Academic Press, 2001), pp. 1-50.

Garbay, C.

F. Bellet, M. Salotti, and C. Garbay, “Une approche opportuniste et coopérative pour la vision de bas niveau,” Trait. Signal 12, 479-494 (1995).

Gardner, M. E.

M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
[CrossRef]

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).

He, D.-C.

D.-C. He, L. Wang, and M. Amani, “A new technique for multi-resolution image fusion,” in Proceedings of IGARSS-International Geoscience and Remote Sensing Symposium (IEEE, 2004), pp. 19-26.

He, D-C.

I. Sebari and D-C. He, “Les approches de segmentation d'image par coopération régions-contours,” Télédétection 7, 499-506 (2007).

Herold, M.

M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
[CrossRef]

Jiang, X. H.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
[CrossRef]

Kaufmann, H.

M. Mueller, K. Segl, and H. Kaufmann, “Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,” Pattern Recogn. 37, 1619-1628 (2004).
[CrossRef]

Kermad, C. D.

C. D. Kermad and K. Chehdi, “Segmentation d'images : recherche d'une mise en oeuvre automatique par coopération de méthodes,” Trait. Signal 15, 331-336 (1998).

Koschan, A.

W. Skarbek and A. Koschan, “Colour Image Segmentation: A Survey,” technical report, Technical University of Berlin, 1994.

Latt, S.

G. Zack, W. Rogers, and S. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem. 25, 741-753 (1977).
[CrossRef] [PubMed]

Lattuati, V.

D. Zugaj and V. Lattuati, “A new approach of color images segmentation based on fusing region and edge segmentations outputs,” Pattern Recogn. 31, 105-113 (1998).
[CrossRef]

Lee, S. W.

R. Bajcsy, S. W. Lee, and A. Leonardis, “Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation,” Int. J. Comput. Vis. 17, 241-272(1996).
[CrossRef]

Leonardis, A.

R. Bajcsy, S. W. Lee, and A. Leonardis, “Detection of diffuse and specular interface reflections and inter-reflections by color image segmentation,” Int. J. Comput. Vis. 17, 241-272(1996).
[CrossRef]

Levine, M. D.

A. M. Nazif and M. D. Levine, “Low level image segmentation: an expert system,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 555-577 (1984).
[CrossRef]

Li, C.-T.

C.-T. Li, “Multiresolution image segmentation integrating Gibbs sampler and region merging algorithm,” Signal Process. 83, 67-78 (2003).
[CrossRef]

Lucchese, L.

L. Lucchese and S. K. Mitra, “Color image segmentation: a state-of-the-art survey,” in Proceedings of the Indian National Science Academy (INSA-A) (New Delhi, India, 2001), pp. 207-221.

Marti, J.

X. Cufi, X. Muñoz, J. Freixenet, and J. Marti, “A review on image segmentation techniques integrating region and boundary information,” in Advances in Imaging and Electron Physics, P.W.Hawkes, ed. (Academic Press, 2001), pp. 1-50.

Martí, J.

X. Muñoz, X. Cufí, J. Freixenet, and J. Martí, “A New approach to segmentation based on fusing circumscribed contours, region growing and clustering,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2000), pp. 800-803.

Martì, J.

X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
[CrossRef]

Matsuyama, T.

M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Comput. Graph. Image Process. 9, 394-407 (1979).
[CrossRef]

M. Nagao and T. Matsuyama, A Structural Analysis of Complex Aerial Photographs (Plenum, 1980).

Mitra, S. K.

L. Lucchese and S. K. Mitra, “Color image segmentation: a state-of-the-art survey,” in Proceedings of the Indian National Science Academy (INSA-A) (New Delhi, India, 2001), pp. 207-221.

Moghaddamzadeh, A.

A. Moghaddamzadeh and N. Bourbakis, “A fuzzy region growing approach for segmentation of color images,” Pattern Recogn. 30, 867-881 (1997).
[CrossRef]

Mueller, M.

M. Mueller, K. Segl, and H. Kaufmann, “Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,” Pattern Recogn. 37, 1619-1628 (2004).
[CrossRef]

Muñoz, X.

X. Muñoz, J. Freixenet, X. Cufi, and J. Martì, “Strategies for image segmentation combining region and boundary information,” Pattern Recogn. Lett. 24, 375-392 (2003).
[CrossRef]

X. Cufi, X. Muñoz, J. Freixenet, and J. Marti, “A review on image segmentation techniques integrating region and boundary information,” in Advances in Imaging and Electron Physics, P.W.Hawkes, ed. (Academic Press, 2001), pp. 1-50.

X. Muñoz, X. Cufí, J. Freixenet, and J. Martí, “A New approach to segmentation based on fusing circumscribed contours, region growing and clustering,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2000), pp. 800-803.

Nagao, M.

M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Comput. Graph. Image Process. 9, 394-407 (1979).
[CrossRef]

M. Nagao and T. Matsuyama, A Structural Analysis of Complex Aerial Photographs (Plenum, 1980).

Nazif, A. M.

A. M. Nazif and M. D. Levine, “Low level image segmentation: an expert system,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 555-577 (1984).
[CrossRef]

Pal, N. R.

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recogn. 26, 1277-1294 (1993).
[CrossRef]

Pal, S. K.

N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recogn. 26, 1277-1294 (1993).
[CrossRef]

Plataniotis, K. N.

K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications (Springer-Verlag, 2000).

Pointet, A.

R. Caloz and A. Pointet, “Analyse comparative de la classification contextuelle et du maximum de vraisemblance: synthèse et cas d'étude,” Télédétection 3, 311-322(2003).

Rayner, P. J. W.

S. A. Barker and P. J. W. Rayner, “Unsupervised image segmentation using Markov random field models,” Pattern Recogn. 33, 587-602 (2000).
[CrossRef]

Roberts, D. A.

M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
[CrossRef]

Rogers, W.

G. Zack, W. Rogers, and S. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem. 25, 741-753 (1977).
[CrossRef] [PubMed]

Salotti, J. M.

J. M. Salotti, “Gestion des informations dans les premières étapes de la vision par ordinateur,” Ph.D. thesis (Institut National Polytechnique de Grenoble, 1994).

Salotti, M.

F. Bellet, M. Salotti, and C. Garbay, “Une approche opportuniste et coopérative pour la vision de bas niveau,” Trait. Signal 12, 479-494 (1995).

Sebari, I.

I. Sebari and D-C. He, “Les approches de segmentation d'image par coopération régions-contours,” Télédétection 7, 499-506 (2007).

Segl, K.

M. Mueller, K. Segl, and H. Kaufmann, “Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery,” Pattern Recogn. 37, 1619-1628 (2004).
[CrossRef]

Skarbek, W.

W. Skarbek and A. Koschan, “Colour Image Segmentation: A Survey,” technical report, Technical University of Berlin, 1994.

Sun, Y.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
[CrossRef]

Venetsanopoulos, A. N.

K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications (Springer-Verlag, 2000).

Wang, J.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recogn. 34, 2259-2281 (2001).
[CrossRef]

Wang, L.

D.-C. He, L. Wang, and M. Amani, “A new technique for multi-resolution image fusion,” in Proceedings of IGARSS-International Geoscience and Remote Sensing Symposium (IEEE, 2004), pp. 19-26.

Wolff, E.

A. P. Carleer, O. Debeir, and E. Wolff, “Assessment of very high spatial resolution satellite image segmentations,” Photogramm. Eng. Remote Sensing 71, 1285-1294 (2005).

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).

Yau, D. K. Y.

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

Zack, G.

G. Zack, W. Rogers, and S. Latt, “Automatic measurement of sister chromatid exchange frequency,” J. Histochem. Cytochem. 25, 741-753 (1977).
[CrossRef] [PubMed]

Zhang, Y.-J.

Y.-J. Zhang, “An overview of image and video segmentation in the last 40 years,” in Advances in Image and Video Segmentation, Y.-J.Zhang, ed. (IRM Press, 2006), pp. 1-15.
[CrossRef]

Zugaj, D.

D. Zugaj and V. Lattuati, “A new approach of color images segmentation based on fusing region and edge segmentations outputs,” Pattern Recogn. 31, 105-113 (1998).
[CrossRef]

Comput. Graph. Image Process. (1)

M. Nagao and T. Matsuyama, “Edge preserving smoothing,” Comput. Graph. Image Process. 9, 394-407 (1979).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

M. Herold, M. E. Gardner, and D. A. Roberts, “Spectral resolution requirements for mapping urban areas,” IEEE Trans. Geosci. Remote Sens. 41, 1907-1919 (2003).
[CrossRef]

IEEE Trans. Image Process. (1)

J. Fan, D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Trans. Image Process. 10, 1454-1466 (2001).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

C. Chu and J. Aggarwal, “The integration of image segmentation maps using region and edge information,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1241-1252 (1993).
[CrossRef]

A. M. Nazif and M. D. Levine, “Low level image segmentation: an expert system,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 555-577 (1984).
[CrossRef]

Int. J. Comput. Vis. (1)

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Figures (13)

Fig. 1
Fig. 1

General flowchart of the proposed segmentation approach.

Fig. 2
Fig. 2

Principle of sequential cooperative region-edge segmentation.

Fig. 3
Fig. 3

Principle of creating a segment using the proposed approach.

Fig. 4
Fig. 4

General shape of the histogram of the differentiated values.

Fig. 5
Fig. 5

Steps for automatic determination of spectral homogeneity threshold.

Fig. 6
Fig. 6

Automatic detection of valley in a bimodal histogram.

Fig. 7
Fig. 7

Masks for the edge-preserving smoothing algorithm [31]: four hexagonal masks, four pentagonal masks, and one 3 × 3 square mask.

Fig. 8
Fig. 8

Comparison of smoothing algorithms: (a) original image, (b) median filter, (c) edge-preserving smoothing algorithm.

Fig. 9
Fig. 9

Example of applying the fusion method proposed by [32] on Ikonos image: (a) multispectral image with a spatial resolution of 4 m ; (b) panchromatic image at 1 m ; (c) image resulting from the fusion of (a) and (b).

Fig. 10
Fig. 10

Result of the proposed approaches for (a) panchromatic, (b) multispectral, (c) edge, and (d) segmented multispectral images.

Fig. 11
Fig. 11

Application of the approach to an image of the urban area with very heterogeneous objects.

Fig. 12
Fig. 12

Application of the approach to images in (a) rural and (b) urban areas.

Fig. 13
Fig. 13

Comparison of spatial profiles on red bands of original image and segmented image.

Tables (3)

Tables Icon

Table 1 Ikonos Specifications

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Table 2 Correlation Matrix Between the Original and Fused Bands [32]

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Table 3 Image Compression Rate with the Proposed Approach

Equations (13)

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h b < T b .
h b = | v i b v j b | ,
d v ( i , j ) = 1 k 1 1 | 1 max | v ( i , j ) v ( i + k , j + l ) | ,
a . x + b . y + c = 0 ,
a = f P 1 f P 2 d v P 1 d v P 2 , b = 1 , c = f p 1 a . d v p 1 .
D p i = | a . x p i + b . y p i + c a 2 + b 2 | .
T = d v p i | D p i  is maximal .
{ ( h 1 < T 1 ) AND AND ( h b < T b ) AND AND ( h n < T n ) } ,   with   b = { 1 , , n } .
sensitivity = TP TP + FN ,
specificity = TN TN + FP ,
overall accuracy = TP + TN TP + FP + FN + TN .
E = [ ( i = 1 Nref j = 1 Nref C i j ) k = 1 Nref C k k ] ( i = 1 Nref j = 1 Nref C i j ) × 100 ,
E Nref = i = 1 Nref ( i = 1 Nref ( c i j c i i ) n iref × 100 ) N ref ,

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