The Conceptual Framework Design of Intelligent Multi-feature Remote Sensing Image Retrieval

  • Center for Earth Observation and Digital Earth, CAS, Beijing 100086, China

Received date: 2010-04-01

  Revised date: 2011-05-28

  Online published: 2011-06-15


With the increasingly improved earth observation systems, the rapid development of various observation techniques and computer intelligence technology, more and more research fields such as environmental monitoring, resource management, disaster forecast, major projects management, national security are highly rely on remote sensing information and its high timeliness. Therefore, we are not only facing the most abundant resources of remote sensing data, but also facing the challenges of transforming the data into information with effective information extraction technology. The Content-based Remote Sensing Image Retrieval (CBRSIR) as one effect approach that obtains remote sensing information quickly is receiving increased attention in the remote sensing application community, and it is emerging as a major research direction. Based on reviewing and analyzing the related technology on CBRSIR, the main bottleneck problems in current research scope are summarized in this paper, which are: one scene remote sensing image always has no main theme or obvious content; difficulty in indexing the multi-feature extracted from remote sensing image; it is difficult to apply multiple features synthetically; the current systems always have low intelligence and efficiency. These problems affect the development of CBRSR. Considering the diversity, complexity and mass of remote sensing images, this paper proposed an intelligent multi-feature integrated remote sensing image retrieval model and constructed its framework. Then the key technology of the multi-feature integrated model, the multi-feature description model, the theoretical framework of intelligent extraction methods and processing algorithms of the multi-feature are also proposed in this paper, and the feature research trend on multi-feature selection and dimensionality reduction, the multi-feature integrated similarity measure model, the intelligent feedback model are also prospected. In the end, this paper discussed the significance of construction of the intelligent multi-feature integrated remote sensing image retrieval method and the main feature work.

Cite this article

DAI Qin, LIU Jianbo, LIU Shibin . The Conceptual Framework Design of Intelligent Multi-feature Remote Sensing Image Retrieval[J]. Journal of Geo-information Science, 2011 , 13(3) : 401 -408 . DOI: 10.3724/SP.J.1047.2011.00401


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