Study On The Key Technology Of Large-scale Content-based Remote Sensing Image Retrieval

With the gradual development and perfection of the global stereo earth observation system, the amount, size, complexity and transmission rate of spatial data which possess the characteristic of globalization, mass and multisource are growing rapidly. The remote sensing image data is the most widely used kind of spatial data. At present, the application of remote sensing technology lags behind its development and results in a tremendous waste of the spatial data resources. This forms the situation that the production and transmission capacity of spatial data is much greater than the analysis capacity of spatial data. So the problems, having significant theoretic and application values, need to be solved urgently are the effective organization and quick application of large-scale remote sensing image data, the quick search of effective space information and the promoting of remote sensing image analysis and recognition accuracy.The key to solve these problems is finding an effective way to manage the spatial data and to retrieve the content of spatial data, which is also the bottleneck of large-scale remote sensing image retrieval in recently years. At present, the study on the technology of content-based image retrieval has made some progress, but on the content-based remote sensing image retrieval the progress is relatively slow, no matter on theoretical system or application system. The research achievement of general image can not be used directly in content-based remote sensing image retrieval because that the remote sensing images are large scale, vague, multidate and semantically rich. The study on the organization, storage, management, description and extraction of data, similarity measure, the network service mode and the design and realization of system architecture are facing many difficulties and shortages for a complete remote sensing image content retrieval system.In this thesis, we put forward some innovative ideas and methods related to the key technology of large-scale content-based remote sensing image retrieval and further verify their value and practicality from theoretical and practical aspect respectively. The innovative achievements and contributions of this thesis are as follows.(1) This thesis puts forward a new method, which combines ECM with FCM, to segment the remote sensing image. Based on this method, we propose a new method for remote sensing image sequence segmentation on the basis of the modified FCM.Combining ECM with FCM, this thesis puts forward a new remote sensing image segmentation method evolving clustering-fuzzy C-means (EC-FCM). ECM is used to choose the initialized center of the fuzzy C-means clustering algorithmic, and then optimize this cluster center by using the FCM to accomplish the division of fuzzy clustering. Finally the genetic clustering can be realized by changing the fuzzy clustering into the certain category through the defuzzification.On the basis of the proposed theory, this thesis further puts forward a new method of the remote sensing image sequence segmentation based on the modified FCM (SSM). This method adopts the low relativity HSI space, the Mahalanobis distances which is more suitable for the remote sensing image. According to this method evolutionary clustering is used to choose the initialized center of the FCM algorithmic, further the image is segmented according to the strategies.Both the theoretical analysis and the results of experiment show that the proposed method, compared with FCM algorithmic, can converge to the global optimum solution with few iterative times, can effectively improve the precision and efficiency of remote sensing image threshold segmentation, and can be applied in the classification of remote sensing image and content-based remote sensing image retrieval system.(2) Based on the granular computing, this thesis proposes a new method of image region similarity measure (IRSM) which can be used in content retrieval. This thesis proposes a new method of image region similarity measure (IRSM) which can be used in content retrieval on the ground of the granular computing. On the basis of the granular computing theory we convert the characteristics information of the image into the ordered matrix. Then the conception of feature granular, ? -order granular base are introduced based on the study of ordered matrix. The importance of the image features are analyzed from the different level of granularity so as to keep the order relation among the regions in the image feature information list. Further the weight of the image feature is given.An example shows that using this method we can measure the image region similarity objectively. Further this method provides a new way to use granular computing theory in the study of the content-based remote sensing image retrieval.(3) This thesis proposes a new spatial subdivision data storage and scheduling service model in the G/S mode.Combined with the service mode of client aggregation services and the global subdivision theory, this thesis proposes a new spatial subdivision data storage and scheduling service model in the G/S mode, provides the framework, the data access process of the spatial subdivision data network service system, designs the address coding stricture and the address resolution process of the spatial subdivision data storage and scheduling service model, shapes the mechanism of management, integration and scheduling of spatial subdivision data. The proposed method is partly verified through the prototype testing, and the verification results indicate that this prototype has a high data access speed, can update easily, and is especially suitable for the large-scale remote sensing image data, beside this method can effectively solve the bottleneck of organizational efficiency and the quick application of mass remote sensing image data. We can infer that it has theoretical and practical value in the development of content-based remote sensing image retrieval system.(4) A distributed remote sensing image database which is suit for the content-based retrieval is designed in this thesis, at the same time a content-based remote sensing image retrieval prototype system is built.Based on the Oracle Spatialand and GeoRaster, a distributed remote sensing image database which is suit for the content-based retrieval is designed in this thesis. A CBRSIR prototype system is designed and realized by using VC++ language and Oracle C++ Call Interface to provide certain retrieval functions as the testing environment and actual example of the study.The running results indicate that the efficiency of the system depends on the size of the server memory, the system consumes less network bandwidth, and the retrieval performance is definitely improved.