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A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

机译：使用VHR影像绘制异质城市环境的局部分割参数优化方法

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摘要

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.
机译：使用基于对象的图像分析（OBIA）绘制大型异类城市区域仍然具有挑战性，尤其是在分割过程方面。这既可以通过不同种类的土地覆盖类别的复杂排列，也可以通过在整个场景中可能遇到的高度多样的城市模式来解释。在这种情况下，使用单个分割参数来获得整个场景令人满意的分割结果可能是不可能的。但是，有可能将整个城市细分为较小的局部区域，根据其城市格局而划分为同质区域。然后，这些区域可用于局部优化分割参数，而不是使用整个图像或单个代表性空间子集。与全局方法相比，本文评估了局部方法对细分参数优化的贡献。位于撒哈拉以南非洲的瓦加杜古被用作案例研究。首先，使用单个全局优化的分割参数对整个场景进行分割。其次，该城市分为283个局部区域，建筑面积和建筑密度均一。然后，使用局部优化的分割参数对每个局部区域进行分割。无监督分割参数优化（USPO）依赖于趋于最大化对象内同质性和对象间异质性的优化功能，用于为这两种方法自动选择分割参数。最后，使用随机森林（RF）分类器执行土地用途/土地覆盖物分类。结果表明，局部方法优于全局方法，特别是通过限制建筑物及其裸土邻居之间的混淆。

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  • 来源
  • 会议地点 Warsaw(PL)
  • 作者

    Tais Grippa; Stefanos Georganos; Moritz Lennert; Sabine Vanhuysse; Eleonore Wolff;

  • 作者单位

    Department of Geosciences Environment and Society - Institute for Environmental Management and Land-use Planning (DGES-IGEAT), Universite libre de Bruxelles, Belgium;

    Department of Geosciences Environment and Society - Institute for Environmental Management and Land-use Planning (DGES-IGEAT), Universite libre de Bruxelles, Belgium;

    Department of Geosciences Environment and Society - Institute for Environmental Management and Land-use Planning (DGES-IGEAT), Universite libre de Bruxelles, Belgium;

    Department of Geosciences Environment and Society - Institute for Environmental Management and Land-use Planning (DGES-IGEAT), Universite libre de Bruxelles, Belgium;

    Department of Geosciences Environment and Society - Institute for Environmental Management and Land-use Planning (DGES-IGEAT), Universite libre de Bruxelles, Belgium;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Object Based Image Analysis; Unsupervised Segmentation Parameters Optimization; Local Approach; Urban Area; Land Cover Mapping;

    机译：基于对象的图像分析；无监督分割参数优化；本地方法；市区;土地覆盖图;

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