| 引用本文: | 郑忠刚,付琨,徐崇彦,等.遥感数据用户需求融合处理技术.[J].国防科技大学学报,2019,41(2):115-123.[点击复制] |
| ZHENG Zhonggang,FU Kun,XU Chongyan,et al.Remote sensing data user request merging technology[J].Journal of National University of Defense Technology,2019,41(2):115-123[点击复制] |
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| 遥感数据用户需求融合处理技术 |
| 郑忠刚1, 付琨2, 徐崇彦1, 巫震宇1, 周长飞1 |
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(1. 北京遥感信息研究所, 北京 100192;2. 中国科学院 电子学研究所, 北京100190)
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| 摘要: |
| 遥感数据是国家的基础性和战略性资源，在经济建设、国防建设、抢险救灾、生态环境保护等方面得到了广泛的应用，发挥着越来越重要的作用，各行各业对遥感数据的需求也越来越多。因此，如何提高对地观测资源的利用率，提高服务响应速度成为迫切需要解决的问题。采用自然语言处理技术，提出了一种用户需求融合处理方法，该方法可以有效地融合归并相同或者相似的用户需求，实现一图多用，引入需求预测和需求融合技术以提高需求融合效率，从而提高对地观测资源的利用率，达到事半功倍的效果。 |
| 关键词: 遥感数据；需求融合；自然语言处理；聚类 语义转换；需求预测；需求挖掘 |
| DOI：10.11887/j.cn.201902017 |
| 投稿日期：2018-02-28 |
| 基金项目:国家自然科学基金资助项目(41801349) |
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| Remote sensing data user request merging technology |
| ZHENG Zhonggang1, FU Kun2, XU Chongyan1, WU Zhenyu1, ZHOU Changfei1 |
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(1.Beijing Institute of Remote Sensing Information, Beijing 100192, China;2. Institute of Electrics, Chinese Academy of Sciences, Beijing 100190, China)
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| Abstract: |
| Remote sensing data, as national fundamental and strategic resources, plays an important role in economy, state security, environment protection and ecology. Since remote sensing data have been widely used in various industries, there is a large amount of data requests from different users, demanding a lot of valuable EOS (earth observation resources). On one hand, users′ data requests increase constantly. On the other hand, EOS resources are always limited. Our objective is to ease the contradiction between a large amount of users′ requests and limited EOS resources. The key idea is to merge identical or similar users′ requests in order to reduce the total number of requests, and then to use requirement forecasting and requirement mining technology to improve requirement fusion efficiency. It is high likely that different users may share identical or similar data requests, since users may show concerns about the same area of the earth over the same time range. |
| Keywords: remote sensing data request merging natural language processing clustering semantic transformation requirement forecasting requirement mining |
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