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Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic

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DOI: 10.4236/jgis.2010.22011    4,664 Downloads   8,312 Views   Citations

ABSTRACT

Three dimensional object extraction and recognition (OER) from geographic data has been definitely one of more important topic in photogrammetry for quite a long time. Today, the capability of rapid generating high-density DSM increases the supply of geographic information but the discrete nature of the measuring makes more difficult to recognize correctly and to extract 3D objects from these surface. The proposed methodology wants to semi-automate some geographic objects clustering operations, in order to perform the recognition process. The clustering is a subjective process; the same set of data items often needs to be partitioned differently based on the application. Fuzzy logic gives the possibility to use in a mathematical process the uncertain information typical of human reasoning. The concept at the base of our proposal is to use the information contained in Image Matching or LiDAR DSM, and typically understood by the human operator, in a fuzzy recognition process able to combine the different input in order to perform the classification. So the object recognition approach proposed in our workflow integrates 3D structural descriptive components of objects, extracted from DSM, into a fuzzy reasoning process in order to exploit more fully all available information, which can contribute to the extraction and recognition process and, to handling the object’s vagueness. The recognition algorithm has been tested with to different data set and different objectives. An important issue is to apply the typical human process which allows to recognize objects in a range image in a fuzzy reasoning process. The investigations presented here have given a first demonstration of the capability of this approach.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

F. Prandi, R. Brumana and F. Fassi, "Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic," Journal of Geographic Information System, Vol. 2 No. 2, 2010, pp. 55-62. doi: 10.4236/jgis.2010.22011.

References

[1] F. Samadzadegan, A. Azizi, M. T. Hahn and C. Lucas, “Automatic 3D Object Recognition and Reconstruction Based on Neuro-Fuzzy Modelling,” ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 59, 2005, pp. 255-277.
[2] M. Hahn and C. Stätter, “A Scene Labeling Strategy for Terrain Feature Extraction Using Multisource Data,” International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3/1, 1998, pp. 435-443.
[3] T. Knudsen and B. Olsen, “Automated Change Detection for Updates of Digital Map Databases,” Photogrammetric Engineering & Remote Sensing, Vol. 69, No. 11, 2003, pp. 1289-1296.
[4] G. Sohn and I. J. Dowman, “Terrain Surface Reconstruction by the Use of Tetrahedron Model with the MDL Criterion, International Archives of Photogrammetry,” Remote Sensing and Spatial Information Sciences, Vol. 34, Part 3A, 2002, pp. 336-344.
[5] G. Vosselmann, “Slope Based Filtering of Laser Altimetry Data,” International Archives of Photogrammetry and Remote Sensing, Amsterdam, Vol. 33(B3), 2000, pp. 935-942.
[6] M. A. Brovelli, M. Cannata and U. M. Longoni, “Managing and Processing LIDAR Data within GRASS,” Proceedings of the Open Source GIS-GRASS Users Conference, Trento, September 2002.
[7] M. Roggero, “Object Segmentation with Region Growing and Principal Component Analysis,” International Archives of Photogrammetry and Remote Sensing and Spatial Information Sciences, Vol. 34, Part 3A, 2002, pp. 289-294.
[8] G. Forlani, C. Nardinocchi, M. Scaioni and P. Zingaretti, “Complete Classification of Raw LIDAR Data and 3D Reconstruction of Buildings,” Pattern Analysis Application, Vol. 8, No. 4, 2006, pp. 357-374.
[9] C. Baillard, “A Hybrid Method for Deriving DTMs from Urban DEMs,” International Archives of Photogrammetry and Remote Sensing, Vol. 37, Part B3b, 2008, pp. 109-113.
[10] R. L. Cannon, J. V. Dave, J. C. Bezdek and M. M. Trivedi, “Segmentation of a Thematic Mapper Image Using the Fuzzy C-Means Clustering Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 24, No. 3, 1986, pp. 400-408.
[11] F. Melgani, B. A. R. Al Hashemy and S. M. R. Taha, “An Explicit Fuzzy Supervised Classification Method for Multispectral Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 1, January 2000, pp. 287-295.
[12] J. Amini, C. Lucas, M. Saradjian, A. Azizi and S. Sa- deghian, “Fuzzy Logic System for Road Identification Using Ikonos Images,” Photogrammetric Record, Vol. 17, No. 99, 2002, pp. 493-503.
[13] A. Mohammadzadeh, A. Tavakoli and M. J. V. Zoej, “Automatic Linear Feature Extraction of Iranian Roads from High Resolution Multi-Spectral Satellite Imagery,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 35, part B3, 2004, p. 764 ff.
[14] B. Wuest and Y. Zhang, “Region Based Segmentation Of Quickbird Imagery Through Fuzzy Integration,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 35, Part B7, 2008, p. 491 ff.
[15] T. Vögtle and E. Steinle, “On the Quality of Object Classification and Automated Building Modelling Based on Laser-Scanning Data International Archives of Photogrammetry,” Remote Sensing and photogrammetry, 2003.

  
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