Research And Application Of Retrieval Strategy For High Resolution Remote Sensing Image Data

With the rapid development of remote sensing technology,the remote sensing image data has grown explosively.At the same time,the demand for remote sensing image data in various industrial fields is also growing.Faced with such large-scale of massive remote sensing image data,how to efficiently retrieve the image data required by users has become a key issue that needs urgently to be solved in the field of spatial information science.However,in the current trend of application of tiled remote sensing data,how to quickly draw a map based on the retrieved tile data is the most common application requirement in the field of remote sensing applications,and a full coverage retrieval is the key to drawing a map.However,most of the studies are mainly focused on the coverage of remote sensing images,parallel data retrieval and so on.There is still relatively little research on full-coverage retrieval of remote sensing tile data;As the first high-resolution multi-polarization Synthetic Aperture Radar(SAR)satellite independently developed in China,GeoFen-3(GF-3)satellite has been widely used in marine,water conservancy and other industries with its all-weather,all-day,anytime,anywhere imaging capability.However,there are still deficiencies in the research on the storage,management,efficient retrieval and other aspects of GF-3 remote sensing image data.Aiming at the above issues,this paper has developed a retrieval strategy for high-resolution remote sensing image data.The retrieval strategy mainly focuses on multi-spectral high-resolution remote sensing image data and GF-3 SAR image data.The main research contents and focus of this paper are as follows:1.A full-coverage retrieval strategy for remote sensing tile data is proposed based on multi-spectral high-resolution remote sensing image data.With the application trend of tiled remote sensing data,on the basis of the Five Layers Fifteen Levels remote sensing image data segmentation organization model,remote sensing tile data storage management model and the traditional remote sensing tile data retrieval methods,and based on the unique characteristics of tile data,which is segmented from multi-spectral high-resolution remote sensing image data,target spatial data distribution retrieval,a full-coverage automatic retrieval and single-grid temporal retrieval are performed on the remote sensing tile data of the target region.The retrieval strategy solves the problems of low retrieval efficiency and cumbersome operations faced by traditional remote sensing tile data retrieval methods.Therefore,it can efficiently and intelligently retrieve a full-coverage remote sensing tile data in the target area that meets user requirements.2.Based on GF-3 SAR image data,a spatial retrieval strategy for remote sensing image data based on stored process is proposed.Based on the deep analysis of the features of the GF series remote sensing image data and the existing remote sensing image data retrieval techniques,the GF-3 SAR image data is stored and managed based on the characteristics and application requirements of the GF-3 SAR image data.The spatial visualization retrieval of the GF-3 SAR image data of the storage management process enables the efficient,convenient,and accurate retrieval of the required GF-3 SAR image data from large-scale remote sensing image data.3.The practical application of the retrieval strategy.In this paper,the above-mentioned two kinds of retrieval strategies are applied to the sub-topic “Major projects for high-resolution earth observation systems”--High-resolution application integrated database system.Through the practical application of the experimental results and its application effects in the system,to verify the effectiveness and efficiency of the proposed retrieval strategy.The research results show that the retrieval strategy for remote sensing image data,which proposed in this paper can automatically,efficiently,and conveniently retrieve the remote sensing image data that meets the user's needs in the target area.What's more,it provides a new retrieval idea and method for the retrieval of massive remote sensing image data.At the same time,it has very important social value and practical significance in promoting the wide application of remote sensing image data in China.