High Resolution Images: Segmenting, Extracting Information and GIS Integration
As the world changes more rapidly, the demand for update information for resource management, environment monitoring, planning are increasing exponentially. Integration of Remote Sensing with GIS technology will significantly promote the ability for addressing these concerns. This paper presents an alternative way of update GIS applications using image processing and high resolution images. We show a method of high-resolution image segmentation using graphs and morphological operations, where a preprocessing step (watershed operation) is required. A morphological process is then applied using the opening and closing operations. After this segmentation we can extract significant cartographic elements such as urban areas, streets or green areas. The result of this segmentation and this extraction is then used to update GIS applications. Some examples are shown using aerial photography.
[1] R. Gonzalez, and R. Woods, Digital Image Processing, Addison-
Wesley, 1993.
[2] S. K Pal et al., "A review on image segmentation techniques", Pattern
Recognition, 29, 1277-1294, 1993.
[3] A. Marion, Introduction aux techniques de traitement d-images, Ed.
Eyrolles, 1987.
[4] R. Horaud and O. Monga, Vision par ordinateur, Ed. Hermes, 1993.
[5] A. Mohhaddamzadeh, and N. Burbakis, "A fuzzy region growing
approach for segmentation color images", Pattern Recognition, 30, 867-
881, 1997.
[6] W. J. W Cheng, "Color image segmentation: advances and prospects ",
Pattern Recognition, 34, 2259-2281, 2001.
[7] S. H Ong et al. "Segmentation of color images using a two-stage selforganizing
network", Image and Vision Computing, 20, 279-289, 2002.
[8] R. Vuduc, "Image segmentation using fractal dimension", Rapport
Technique, Cornell University, 1997.
[9] M. Kass et al. "Snakes: Active contour models", Computer Vision
Graphics and Image Processing, 321-331, 1998.
[10] C. Soh, "Segmentation of satellite imagery of natural scenes using data
mining", IEEE Transactions on Geosciences and remote sensing,37,
1086-1099, 1999.
[11] M. Baatz, and A. Shape, "Multiresolution segmentation: an optimization
approach for high quality multi-scale segmentation", Applied
Geographical data processing, 12-23, 2000.
[12] J. Serra, Image Analysis and Mathematical Morphology, Academic
Press, London, 1982.
[13] J. Serra, and P. Soille, Mathematical Morphology and its applications to
image processing, Kluwer Academic Publishers, 1994.
[14] L. Vincent. and. Soille, "Watersheds in Digital Spaces: An Efficient
Algorithm based on Immersion simulations". IEEE Transactions on
Pattern Analysis and Machine Intelligence, 583-598, 1991.
[15] J. Chassery and A. Montanvert, Geométrie discrète en analyse d-images,
Ed. Hermes, 1991.
[16] L. Vincent, "Graphs and mathematical morphology", Signal Processing,
16, 365-388, 1989.
[17] G. Flouzat and O. Amram, "Segmentation d-images satelitaires par
analyse morphologique spatiale et spectrale", Acta Stereologica, 16,
267-274, 1997.
[18] D. Papadias and Y. Theodoridis. "Spatial relations, minimum bounding
rectangles, and spatial data structures". International Journal of
Geographical Information Science, 11(2) :111-138, 1997.
[19] J. Gero. Representation and reasoning about shapes : Cognitive and
computational studies in visual reasoning in design. Spatial Information
Theory, Springer, pages315-330, 1999
[1] R. Gonzalez, and R. Woods, Digital Image Processing, Addison-
Wesley, 1993.
[2] S. K Pal et al., "A review on image segmentation techniques", Pattern
Recognition, 29, 1277-1294, 1993.
[3] A. Marion, Introduction aux techniques de traitement d-images, Ed.
Eyrolles, 1987.
[4] R. Horaud and O. Monga, Vision par ordinateur, Ed. Hermes, 1993.
[5] A. Mohhaddamzadeh, and N. Burbakis, "A fuzzy region growing
approach for segmentation color images", Pattern Recognition, 30, 867-
881, 1997.
[6] W. J. W Cheng, "Color image segmentation: advances and prospects ",
Pattern Recognition, 34, 2259-2281, 2001.
[7] S. H Ong et al. "Segmentation of color images using a two-stage selforganizing
network", Image and Vision Computing, 20, 279-289, 2002.
[8] R. Vuduc, "Image segmentation using fractal dimension", Rapport
Technique, Cornell University, 1997.
[9] M. Kass et al. "Snakes: Active contour models", Computer Vision
Graphics and Image Processing, 321-331, 1998.
[10] C. Soh, "Segmentation of satellite imagery of natural scenes using data
mining", IEEE Transactions on Geosciences and remote sensing,37,
1086-1099, 1999.
[11] M. Baatz, and A. Shape, "Multiresolution segmentation: an optimization
approach for high quality multi-scale segmentation", Applied
Geographical data processing, 12-23, 2000.
[12] J. Serra, Image Analysis and Mathematical Morphology, Academic
Press, London, 1982.
[13] J. Serra, and P. Soille, Mathematical Morphology and its applications to
image processing, Kluwer Academic Publishers, 1994.
[14] L. Vincent. and. Soille, "Watersheds in Digital Spaces: An Efficient
Algorithm based on Immersion simulations". IEEE Transactions on
Pattern Analysis and Machine Intelligence, 583-598, 1991.
[15] J. Chassery and A. Montanvert, Geométrie discrète en analyse d-images,
Ed. Hermes, 1991.
[16] L. Vincent, "Graphs and mathematical morphology", Signal Processing,
16, 365-388, 1989.
[17] G. Flouzat and O. Amram, "Segmentation d-images satelitaires par
analyse morphologique spatiale et spectrale", Acta Stereologica, 16,
267-274, 1997.
[18] D. Papadias and Y. Theodoridis. "Spatial relations, minimum bounding
rectangles, and spatial data structures". International Journal of
Geographical Information Science, 11(2) :111-138, 1997.
[19] J. Gero. Representation and reasoning about shapes : Cognitive and
computational studies in visual reasoning in design. Spatial Information
Theory, Springer, pages315-330, 1999
@article{"International Journal of Information, Control and Computer Sciences:50242", author = "Erick López-Ornelas", title = "High Resolution Images: Segmenting, Extracting Information and GIS Integration", abstract = "As the world changes more rapidly, the demand for update information for resource management, environment monitoring, planning are increasing exponentially. Integration of Remote Sensing with GIS technology will significantly promote the ability for addressing these concerns. This paper presents an alternative way of update GIS applications using image processing and high resolution images. We show a method of high-resolution image segmentation using graphs and morphological operations, where a preprocessing step (watershed operation) is required. A morphological process is then applied using the opening and closing operations. After this segmentation we can extract significant cartographic elements such as urban areas, streets or green areas. The result of this segmentation and this extraction is then used to update GIS applications. Some examples are shown using aerial photography.
", keywords = "GIS, Remote Sensing, image segmentation, feature extraction.", volume = "3", number = "6", pages = "1488-6", }
