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首页> 外文期刊>ISPRS International Journal of Geo-Information >Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis
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Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis

机译：评估本体在基于地理对象的图像分析中的适用性

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摘要

In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL) is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL) and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides.
机译：在基于地理对象的图像分析（GEOBIA）中，通常使用适当领域知识支持的基于规则的分类技术来实现图像对象的识别。但是，GEOBIA当前缺乏将图像对象识别所需的领域知识形式化的系统方法。本体论提供了表征领域特定类的表述词汇。这项研究提出了一个本体论框架，将领域知识概念化，以支持基于规则的分类的应用。所提出的本体论框架已通过滑坡案例研究进行了测试。 Web本体语言（OWL）用于在滑坡域中构建本体。具有提取特征的分割图像对象作为实例被合并到本体中。分类规则以语义网规则语言（SWRL）编写，并使用语义推理器执行以将实例分配给适当的滑坡类。机器学习技术用于预测规则中要素属性的新阈值。我们的框架与未使用本体进行图像分类的已发表的滑坡检测工作进行了比较。我们的结果表明，从本体论框架得出的分类符合非本体论方法。本研究以本体论方法为基准，为滑坡案例研究提供了一种图像分类的替代方法。

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