| Grant number: | 12/22332-2 |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| Start date: | March 01, 2013 |
| End date: | December 31, 2016 |
| Field of knowledge: | Physical Sciences and Mathematics - Geosciences - Geodesy |
| Principal Investigator: | Aluir Porfírio Dal Poz |
| Grantee: | Vanessa Jordão Marcato Fernandes |
| Host Institution: | Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil |
Abstract This paper proposes a method for the automatic extraction of building roof contours through the combination of LiDAR data and photogrammetric data, and Markov Random Field (MRF). Initially, a normalized digital surface model (nDSM) is generated on the basis of the difference between the digital surface model and the digital terrain model, obtained from the LiDAR point cloud. Then the nDSM is segmented to obtain the polygons representing aboveground objects. These polygons are projected onto image to restrict the search space for image segmentation into regions. This process enables the extraction of polygons in the image representing aboveground objects. To minimize the over-segmentation problem of the image, a Gaussian color invariance model is applied, which aims to eliminate problems of lighting differences within the same object. Building roof contours are identified from among the aboveground objects in the image by optimizing a Markov-random-field-based energy function that embodies roof contour specific properties. Polygons extracted from image and from LiDAR data are used in the MRF model. Therefore, the great challenge of this work is the development of an energy function that responds to configurations of polygons representing, ideally, only building roof contours. The proposed method will be assessed based on real data - high-resolution aerial images and 3D data from LiDAR scanning. | |
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