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Proceedings Paper

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery
Author(s): Tais Grippa; Stefanos Georganos; Moritz Lennert; Sabine Vanhuysse; Eléonore Wolff
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Paper Abstract

Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

Paper Details

Date Published: 4 October 2017
PDF: 19 pages
Proc. SPIE 10431, Remote Sensing Technologies and Applications in Urban Environments II, 104310G (4 October 2017); doi: 10.1117/12.2278422
Show Author Affiliations
Tais Grippa, Univ. Libre de Bruxelles (Belgium)
Stefanos Georganos, Univ. Libre de Bruxelles (Belgium)
Moritz Lennert, Univ. Libre de Bruxelles (Belgium)
Sabine Vanhuysse, Univ. Libre de Bruxelles (Belgium)
Eléonore Wolff, Univ. Libre de Bruxelles (Belgium)


Published in SPIE Proceedings Vol. 10431:
Remote Sensing Technologies and Applications in Urban Environments II
Thilo Erbertseder; Nektarios Chrysoulakis; Ying Zhang; Wieke Heldens, Editor(s)

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