Research Of Remote Sensing Image Data Mining Technology

The volume of remote sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. Image mining is armed to find the implicit and ambiguous informations, relationship of image data or other patterns in database.Remote sensing image data mining isn't only the applications of image mining in the field of remote sensing, but also a important embranchment of the spacial data mining.It uses the ecumenical theory and technology of image mining as well as combines the particularity of remote sensing image and spacial data, such as the particular spacial locations, the complicated spatial relations and the spatial scale. It is a important crossed research subject between the spacial data mining and image mining. Finally, the method of classification and forecast is an important research field in the remote sensing image analysing and mining, also, is a reaearch focus of this dissertation.Encircling a key of remote sensing image information automatically acquired and used, mainly studies the theory and technology in remote sensing image data mining. The main reaearch results and creative proposals are following :(1) Studying two frameworks for image mining---an information-driven frameworkand a funtion-driven framework, presents a framework figure of a prototype system for remote sensing image mining and a flow chart of it. Also, points out necessaryfunctions and tools for the system.(2) Achieves the algorithm of two frequently used classification implement—a supervised and non.supervised, bayes and isodata algorithm. Also improves on the bayes algorithm. Achieves texture feature extraction by using gray-level co-occurrence matrices. Studys the special structure and relations of image objects.(3) On the basis of studying of a few of data mining theories, including fuzzy classification, evidence theory, artificial nerve network (BP and SOM), support vector machines, association rules and decision-tree algorithm, presents the method of remote sening mining based on the theorys.(4) The above-mentioned methods of data mining almost aims at the image analyzing of pixels. The information acquiring from pixels is greadtly limited, which don't show the contextual information of neighboring pixels. In the dissertation, presents the " Object Oriented "image mining concept for the first time. Also, designs the flow and algorithm of the method and realizes it.(5) In research of the information of expression, including semantic feature, such as color, texture, border, as so on, spectral mixture feature, high- dimension data feature extraction,Gis data and expert information,presents two approach by using Gis data for image mining—straightway classifying in form of logistic waveband and systematizing applicat ion in the spatial database. Designs the using model or system framework. Points out mining Language would be the same with geographical information mining, resembling SQL input request Language—GMQL (Geo-Mining Query Language). Presents a expression method of a knowledge.data base. Realizes the remote sensing image data mining based on rule. Realizes high, dimension data feature extraction algorithm by using debasing dimension of data.(6) After analyzing the actuality of web data mining, brings forward a framework of the web image mining system. On second thoughts, puts forward a framework of the web Remote sensing image mining system.