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Remote Sensing Technology and Application  2020, Vol. 35 Issue (3): 685-693    DOI: 10.11873/j.issn.1004-0323.2020.3.0685
    
Design of Content-based Remote Sensing Image Change Information Retrieval and Relevance Feedback Model
Caihong Ma1,2(),Linlin Guan2,Fu Chen2(),Dacheng Wang3,Jianbo Liu2
1.Sanya Institute of Remote Sensing, Sanya, Hainan 572029, China
2.Aeraspace Information Research Institute, China Remote Sensing Satellite Ground Station, Chinese Academy of Sciences, Beijing 100094, China
3.Aeraspace Information Research Institute, National Engineering Application Center, Chinese Academy of Sciences, Beijing 100094, China
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Abstract  

With the rapid development of satellite remote sensing technology, processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve change information what users need from a massive database of images. In the context of mass remote sensing data, the existing knowledge based on a priori knowledge + the keyword / metadata remote sensing data service model can not meet above-mentioned challenge. Firstly,it is not guaranteed to obtain the totally change information data in the database, as we can not get the all prior knowledge. Second, the keyword / metadata can not accurately describe the different application areas of the user's actual retrieval needs. To deal with this, the Content-Based Image Retrieval (CBIR) is successfully applied on the change detection in this paper. And, Content-Based Remote Sensing Image Change Information Retrieval and Relevance Feedback model is introduced. Firstly, we learn the CBIR theory fully and exclusively. Then, the model structure and framework is built. And, some critical issues, such as data management, multi-features selection and relevance feedback, are considered. Thirdly, an experimental prototype system is set up to demonstrate the validity and practicability of this model. As a new remote sensing image change detection information acquisition mode, the new model can reduce the demands of image pre-processing, overcome synonyms spectrum, seasonal changes and other factors in the change detection, and meet different kinds of needs. Meanwhile, the new model has important implications for improving remote sensing image management skill and autonomic capabilities of information retrieval filed.

Key words:  Content-Based Remote Sensing Image Retrieval      Change information detection      Information management      Remote sensing data service     
Received:  24 January 2019      Published:  10 July 2020
ZTFLH:  TP79  
Corresponding Authors:  Fu Chen     E-mail:  mach@radi.ac.cn;chenfu@radi.ac.cn
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Caihong Ma
Linlin Guan
Fu Chen
Dacheng Wang
Jianbo Liu

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Caihong Ma,Linlin Guan,Fu Chen,Dacheng Wang,Jianbo Liu. Design of Content-based Remote Sensing Image Change Information Retrieval and Relevance Feedback Model. Remote Sensing Technology and Application, 2020, 35(3): 685-693.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.3.0685     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I3/685

Fig.1  The overall technical architecture diagram of the content-based remote sensing change information retrieval
Fig.2  The flowchart of the remote sensing image storagment
Fig.3  The schematic diagram of the main interface
Fig.4  Query by example: looking for urban expansion area around water in long time series RS images
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