Extraction of building roof contours from the integration of high-resolution aerial imagery and laser data using Markov random fields

Author(s)

Marcato Fernandes, Vanessa Jordao ^[1] ; Dal Poz, Aluir Porfirio ^[2]

Affiliation

^[1] Fed Univ Grande Dourados, Fac Agr Sci, Rodovia Dourados Itahum, Km 12, BR-79804070 Dourados - Brazil

^[2] Sao Paulo State Univ, Dept Cartog, Presidente Prudente - Brazil

• Document type: Review article
• Source: INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION; v. 9, n. 4, p. 263-286, 2018.
• Web of Science Citations: 0

Abstract

This study proposes a method for the automatic extraction of building roof contours from the integration of airborne laser scanning (ALS) and photogrammetric data using the Markov random field probabilistic approach. Initially, a normalised digital surface model (nDSM) obtained from the ALS point cloud is segmented to obtain the polygons that represent high objects in the scene. These polygons are projected onto the image to delimit regions (sub-images) that will be segmented in the image, which allows the extraction of polygons in the image that represent the corresponding high objects. The polygons that represent roof contours are identified by the optimisation of an energy function that models the geometric and contextual properties of building roof contours via the genetic algorithm. This energy function combines the polygons extracted from the nDSM and from the image. The proposed method was evaluated with real data, including high-resolution aerial images and ALS data. The experimental results showed that the proposed method works properly, exhibits few failures and has average completeness and correctness rates above 90%. (AU)

• FAPESP's process: 12/22332-2 - Extraction of building roof countors through the integration of high-resolution aerial images and LiDAR data, using Markov random field
• Grantee: Vanessa Jordão Marcato Fernandes
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/13138-0 - Automated building boundary extraction using very high-resolution aerial images and laser scanning data
• Grantee: Aluir Porfírio Dal Poz
• Support Opportunities: Regular Research Grants