Study On Remote Sensing Image-base Content Retrieval Based On Database Model

The fast development of remote sensing technology, computer technology and internet technology in 21st century is now making it possible for researchers from various disciplines to acquire needed multi-temporal remote sensing images with both high accuracy and high resolution. In contrast with this, there is a significant lag of the theory of remote sensing image retrieval as well as its relevant technologies. How to effectively obtain the needed information from massive remote sensing image-base is, therefore, of great importance. Starting from the status quo and some existing problems of remote sensing image retrieval, this dissertation aims to: (1)bring forward a universal remote sensing image concept model (URSICM), design a logical object-oriented organization and a data storage schema based on URSICM, (2)raise a new approach of the color and texture fused features based remote sensing image retrieval (CTFFBIR) and discuss the optimized algorithms of URSICM, (3)raise an approach of GIS semantics-based remote sensing image retrieval, (4) design and develop the prototype system RSIQuey, and (5)inject some new ideas into the retrieval and management of remote sensing image base.Some main research contents are listed below:1. The research discusses some critical technologies of remote sensing image -base content retrieval(RSIBCR), including the organization and management of remote sensing image data, the database indexing mechanism, the description and extraction of low-level vision features, the assessment of similarity between, the mechanism of relevance feedback and the evaluation of indexing algorithm. Some confronted difficulties and existing problems have also been identified.2. By analyzing the information's characteristics, the contents provided by remote sensing image and the characteristics and limits of current image data models, this dissertation intends to introduce a universal concept model of remote sensing image data URSICM, which integrates the metadata of remote sensing images, raw pixel information, vision features, image objects and semantic contents into a unified frame, and discusses the URSICM-based logical object-oriented model, data organization and storage schema.3. The research work demonstrates the purpose and significance of image decomposition. After examining the image decomposition methods of Quad-Tree and Nona-Tree, a method of Quin-Tree is put forward. This new method takes the advantages of both Quad-Tree and Nona-Tree, and reaches a more satisfactory balance between the quantity of sub-images and the overlapping ratio of query images and sub-images.4. This dissertation also seeks to analyze the shortcomings of single type vision feature based retrieval and develops a new remote sensing image retrieval method of integrating color and texture fused features CTFFBIR, according to the spectral characteristics of satellite and aviation imagery with high resolution. Based on the use of filtering power values as the convolution of multi-channel 2D Gabor filters and the images, this new method exacts each sub-image's texture feature of filtering power, calculates average and mean square deviation of the color value of the subs-image, takes linear weighted similarity assessment from color and texture features' distance with query image and sub-images holding the same size from the database. In particular, the weighted feature value could be set by people sending the query request, and adjusted by relevant feedbacks.5. In order to improve the retrieval efficiency of CTFFBIR, a cluster-based sub—image classifying indexing algorithm of optimization has been developed. This algorithm would largely reduce the response time of on-line qeuery by clustering each database's sub-images using their 26 dimensional feature of color and texture features and constructing the classifying index of database sub-images in use of the results of clustering.6. An algorithm of dynamic relevance feedbacks is presented by the dissertation. The algorithm improves the Rui method of refreshing multi-level feature weights. The weight of different dimension and different class will be refreshed by evaluating the similarities of sub-images from indexing results during the process of image indexing. Images that have been labeled as uncorrelated in the previous round will not enter into the next similarity assessment, while the ones having been labeled as highly correlated will receive a number of high priority in the next round.7. The dissertation also presents a GIS semantics-based method of remote sensing image retrieval(GISSBIR). This method is able to search for objects of interests by directly making use of GIS's capacity of describing spatial features and spatial relationships. By using the minimum border of these objects, the relevant geospatial scope of these remote sensing image data will be obtained, so that users can complete the task of indexing and produce an applicable alternative of remote sensing image database content indexing. GISSBIR puts special emphasis upon the following two questions: (1) constructing a conceptual semantic network to solve the semantic contradictions between user query request and the system, and (2) developing the retrieval of spatial relationships and making extensions for Oracle Spatial's direction relationship model.8. The dissertation describes the design and implementation of the prototype system. Takes Oracle and Microsoft Visual C++ as its data container and developing platform, this system adapts a distributed architecture of Client/Server. By analyzing the experiment results, CTFFBIR and its optimum algorithm are essentially effective, and the thinking path is accessible.