Building Roof Segmentation from Aerial Images Using a Line-and Region-Based Watershed Segmentation Technique

Sensors

Youssef El Merabet¹  Cyril Meurie³  Yassine Ruichek¹  Abderrahmane Sbihi² 

[1] IRTES-SeT, University of Technology of Belfort-Montbeliard, 13 rue Ernest-Thierry Mieg, 90010 Belfort cedex, France;National School of Applied Sciences of Tangier (ENSAT), Abdemalek Essaadi University, B.P. 1818, 90000 Tangier, Maroc;Univ Lille Nord de France, F-59000 Lille, IFSTTAR, LEOST, F59650 Villeneuve d'Ascq, France

关键词: orthophotoplan;    image segmentation;    watershed;    region merging;    roof segmentation;    2D roof ridge modeling;   

DOI  :  10.3390/s150203172

来源: mdpi

【 摘 要 】

In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.