Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique
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 simplifyin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/2/3172 |
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| author | Youssef El Merabet Cyril Meurie Yassine Ruichek Abderrahmane Sbihi Raja Touahni |
| author_facet | Youssef El Merabet Cyril Meurie Yassine Ruichek Abderrahmane Sbihi Raja Touahni |
| author_sort | Youssef El Merabet |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:06:17Z |
| format | Article |
| id | doaj.art-9da5c6494ca74bdf97c2063a9cb07936 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:06:17Z |
| publishDate | 2015-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9da5c6494ca74bdf97c2063a9cb079362022-12-22T04:00:42ZengMDPI AGSensors1424-82202015-02-011523172320310.3390/s150203172s150203172Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation TechniqueYoussef El Merabet0Cyril Meurie1Yassine Ruichek2Abderrahmane Sbihi3Raja Touahni4IRTES-SeT, University of Technology of Belfort-Montbeliard, 13 rue Ernest-Thierry Mieg, 90010 Belfort cedex, FranceUniv Lille Nord de France, F-59000 Lille, IFSTTAR, LEOST, F59650 Villeneuve d'Ascq, FranceIRTES-SeT, University of Technology of Belfort-Montbeliard, 13 rue Ernest-Thierry Mieg, 90010 Belfort cedex, FranceNational School of Applied Sciences of Tangier (ENSAT), Abdemalek Essaadi University, B.P. 1818, 90000 Tangier, MarocLASTID Laboratory, Département de Physique, Faculté des Sciences, Université Ibn Tofail, B.P 133, 14000 Kénitra, MarocIn 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.http://www.mdpi.com/1424-8220/15/2/3172orthophotoplanimage segmentationwatershedregion mergingroof segmentation2D roof ridge modeling |
| spellingShingle | Youssef El Merabet Cyril Meurie Yassine Ruichek Abderrahmane Sbihi Raja Touahni Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique Sensors orthophotoplan image segmentation watershed region merging roof segmentation 2D roof ridge modeling |
| title | Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique |
| title_full | Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique |
| title_fullStr | Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique |
| title_full_unstemmed | Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique |
| title_short | Building Roof Segmentation from Aerial Images Using a Lineand Region-Based Watershed Segmentation Technique |
| title_sort | building roof segmentation from aerial images using a lineand region based watershed segmentation technique |
| topic | orthophotoplan image segmentation watershed region merging roof segmentation 2D roof ridge modeling |
| url | http://www.mdpi.com/1424-8220/15/2/3172 |
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