Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. Moreover, interpretability of feature extraction becomes a major disadvantage of the state-of-the-art CNN. Especially for the complex urban objects, which varies in geometrical shapes, functional structures, environmental contexts, etc, due to the heterogeneity between low-level data features and high-level semantics, the features derived from remote sensing data alone are limited to facilitate an accurate recognition. In this paper, we present an ontology-based methodology framework for enabling object recognition through rules extracted from the high-level semantics, rather than unexplainable features extracted from a CNN. Firstly, we semantically organize the descriptions and definitions of the object as semantics (RDF-triple rules) through our developed domain ontology. Secondly, we exploit semantic web rule language to propose an encoder model for decomposing the RDF-triple rules based on a multilayer strategy. Then, we map the low-level data features, which are defined from optical satellite image and LiDAR height, to the decomposed parts of RDF-triple rules. Eventually, we apply a probabilistic belief network (PBN) to probabilistically represent the relationships between low-level data features and high-level semantics, as well as a modified TanH function is used to optimize the recognition result. The experimental results on lacking of the training process based on data samples show that our proposed approach can reach an accurate recognition with high-level semantics. This work is conducive to the development of complex urban object recognition toward the fields including multilayer learning algorithms and knowledge graph-based relational reinforcement learning.

中文翻译：

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通过集成视觉特征和可解释语义的基于本体的复杂城市物体识别框架

尽管先前的工作提出了复杂的概率模型，该模型具有从遥感数据（例如，卷积神经网络，CNN）中提取特征的强大能力，但仍需要更多的探索来致力于探索人类在待识别对象上的语义。此外，特征提取的可解释性成为现有技术CNN的主要缺点。特别是对于复杂的城市对象而言，由于低级数据特征和高级语义之间的异质性，其几何形状，功能结构，环境背景等有所不同，因此仅限于遥感数据衍生的特征受到限制，以方便准确识别。在本文中，我们提出了一种基于本体的方法框架，可通过从高级语义中提取的规则（而不是从CNN中提取的无法解释的特征）来实现对象识别。首先，我们通过我们开发的领域本体将对象的描述和定义作为语义（RDF-三重规则）进行语义组织。其次，我们利用语义网络规则语言，提出了一种基于多层策略的RDF三元规则分解编码器模型。然后，我们将从光学卫星图像和LiDAR高度定义的低层数据特征映射到RDF三重规则的分解部分。最终，我们应用概率信念网络（PBN）来概率表示低层数据特征和高层语义之间的关系，以及改进的TanH函数用于优化识别结果。在缺乏基于数据样本的训练过程的情况下的实验结果表明，我们提出的方法可以通过高级语义获得准确的识别。这项工作有利于向复杂的城市物体识别领域发展，包括多层学习算法和基于知识图的关系强化学习。