| 国产遥感影像分类技术应用研究进展综述 |
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| 引用本文： | 胡杰,张莹,谢仕义.国产遥感影像分类技术应用研究进展综述[J].计算机工程与应用,2021,57(3):1-13. |
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| 作者姓名： | 胡杰 张莹 谢仕义 |
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| 作者单位： | 1.广东海洋大学 数学与计算机学院，广东 湛江 524088
2.湛江湾实验室 南海渔业大数据中心，广东 湛江 524088 |
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| 基金项目： | 广东省普通高校重点科研项目"创新强校工程"项目;南方海洋科学与工程广东省实验室(湛江)项目 |
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| 摘 要： | 遥感影像分类技术为我国遥感影像应用于生态建设、绿色发展、乡村振兴、脱贫攻坚和“一带一路”构建等提供了重要的技术支撑，在服务经济社会发展、建设美丽中国、保障民生安全等方面具有重要意义。近年来，大数据、人工智能技术的飞速发展，使得国产遥感影像在分类应用的研究取得重大发展。简要分析了遥感影像分类技术及每阶段存在的问题；对国内主要六个系列遥感卫星数据进行了概述；综合分析了国产遥感影像基于像元的、混合像元的、面向对象的、基于深度学习的四种分类方法，并探讨其在分类应用中的研究进展，通过国产遥感影像分类领域中的应用情况，进一步在方法上对四种分类分别进行比较分析；提出国产遥感影像分类应用中存在的问题，对未来国产遥感影像应用发展的趋势进行了预估。
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| 关 键 词： | 影像分类技术 国产卫星遥感 影像分类应用 深度学习 |
Summary of Research Progress on Application of Domestic Remote Sensing Image Classifi-cation Technology |
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| HU Jie,ZHANG Ying,XIE Shiyi.Summary of Research Progress on Application of Domestic Remote Sensing Image Classifi-cation Technology[J].Computer Engineering and Applications,2021,57(3):1-13. |
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| Authors: | HU Jie ZHANG Ying XIE Shiyi |
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| Affiliation: | 1.College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, Guangdong 524088, China
2.Fisheries Big Data Center of South China Sea, Zhanjiang Bay Laboratory, Zhanjiang, Guangdong 524088, China |
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| Abstract: | Remote sensing image classification technology provides important technical support for the application of the domestic remote sensing images in ecological construction, green development, rural revitalization, poverty alleviation and the construction of the Belt and Road, etc, which is of great significance for serving economic and social development, building a beautiful China, and ensuring the safety of people’s livelihood, etc. In recent years, the rapid development of big data technology and artificial intelligence technology has made great progress in the research on classification application of domestic remote sensing image. This paper briefly analyzes the remote sensing image classification technology and the problems in each stage, and summarizes the data of the six main domestic remote sensing satellites. The four classification methods of domestic remote sensing images based on pixel, mixed pixel, object-oriented and deep learning are comprehensively analyzed, and their research progress in classification application is discussed. Through the application in the field of domestic remote sensing image classification, the four classifications are further compared and analyzed in terms of methods. Finally, it summarizes the existing issues of domestic remote sensing image classification application, and predicts the future directions of domestic remote sensing image application. |
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| Keywords: | image classification technology domestic satellite remote sensing image classification application deep learning |
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