| 耦合知识图谱和深度学习的新一代遥感影像解译范式 |
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| 引用本文： | 李彦胜,张永军.耦合知识图谱和深度学习的新一代遥感影像解译范式[J].武汉大学学报(信息科学版),2022,47(8):1176-1190. |
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| 作者姓名： | 李彦胜 张永军 |
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| 作者单位： | 1.武汉大学遥感信息工程学院，湖北 武汉，430079 |
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| 基金项目： | 国家自然科学基金42030102国家自然科学基金41971284湖北省自然科学基金创新群体2020CFA003 |
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| 摘 要： | 在遥感大数据时代，遥感影像智能解译是挖掘遥感大数据价值并推动若干重大应用的关键技术，如何将知识推理和数据学习两类解译方法有机联合已成为遥感大数据智能处理的重要研究趋势。由此提出了面向遥感影像解译的遥感领域知识图谱构建与进化方法，建立了顾及遥感成像机理和地理学知识的遥感领域知识图谱。在遥感领域知识图谱支撑下，以零样本遥感影像场景分类、可解释遥感影像语义分割以及大幅面遥感影像场景图生成3个典型的遥感影像解译任务为例，研究了耦合知识图谱和深度学习的新一代遥感影像解译范式。在零样本遥感影像场景分类实验中，所提方法在不同的可见类/不可见类比例和不同的语义表示下，都明显优于其他方法；在可解释遥感影像语义分割实验中，知识推理与深度学习的联合方法取得了最好的分类结果；在大幅面遥感影像场景图生成实验中，知识图谱引导的方法精度明显高于基准的频率统计方法。遥感知识图谱推理与深度数据学习的融合可以有效提升遥感影像的解译性能。
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| 关 键 词： | 数据学习与知识推理 遥感知识图谱 深度学习 遥感影像智能解译 |
| 收稿时间： | 2021-11-28 |
A New Paradigm of Remote Sensing Image Interpretation by Coupling Knowledge Graph and Deep Learning |
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| Affiliation: | 1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives In the remote sensing (RS) big data era, intelligent interpretation of remote sensing images (RSI) is the key technology to mine the value of big RS data and promote several important applications. Traditional knowledge-driven RS interpretation methods, represented by expert systems, are highly interpretable, but generally show poor performance due to the interpretation knowledge being difficult to be completely and accurately expressed. With the development of deep learning in computer vision and other fields, it has gradually become the mainstream technology of RSI interpretation. However, the deep learning technique still has some fatal flaws in the RS field, such as poor interpretability and weak generalization ability. In order to overcome these problems, how to effectively combine knowledge inference and data learning has become an important research trend in the field of RS big data intelligent processing. Generally, knowledge inference relies on a strong domain knowledge base, but the research on RS knowledge graph (RS-KG) is very scarce and there is no available large-scale KG database for RSI interpretation now. Methods To overcome the above considerations, this paper focuses on the construction and evolution of the RS-KG for RSI interpretation and establishes the RS-KG takes into account the RS imaging mechanism and geographic knowledge. Supported by KG in the RS field, this paper takes three typical RSI interpretation tasks, namely, zero-shot RSI scene classification, interpretable RSI semantic segmentation, and large-scale RSI scene graph generation, as examples, to discuss the performance of the novel generation RSI interpretation paradigm which couples KG and deep learning. Results and Conclusions A large number of experimental results show that the combination of RS-KG inference and deep data learning can effectively improve the performance of RSI interpretation.The introduction of RS-KG can effectively improve the interpretation accuracy, generalization ability, anti-interference ability, and interpretability of deep learning models. These advantages make RS-KG promising in the novel generation RSI interpretation paradigm. |
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