Research On Key Technologies For Content-based Retrieval From Remote Sensing Image Database

Remote Sensing Data are basic data in digital earth development and its quick browsing and efficient retrieval are important means of remote sensing information extraction and sharing. As an effective means of manage and utilize image database information according to comprehension of images themselves, content-based image retrieval (CBIR) has become one of the most active researches in image databases, computer vision etc. and a key technology of information high way and digital library. CBIR provides new chance to solve the problem of information extraction from large remote sensing image database. However, the diversity and complexity of remote sensing image and the enormous data volume as well are big challenge of valid retrieval from remote sensing image databases, especially under web environment. Content-based retrieval of remote sensing database is a hot topic by integrating multiple disciplines including remote sensing image processing, image databases, computer vision and pattern recognition etc. and has gotten international considerable attention. Research on it has important meaning in theory and practice for promoting remote sensing information acquisition and sharing. This paper intends to put forward some new thoughts and methods on key technologies for content-based retrieval of remote sensing image database and to validate its efficiency and practicability through theory and practice. Main research and concrete work include five aspects:1. Firstly, domestic and foreign research projects and state-of-the-arts of content-based retrieval from remote sensing image databases are induced systematically. Then concerned key technologies are summarized and main obstacles compared with other applications of content-based image retrieval are analyzed. Based on them, key aspects to solution are pointed out.2. Secondly, this paper points out multi-scale texture feature extraction and reasonable strategies of block-based data organization are two important areas to supporttexture-based retrieval from remote sensing image databases. The limitation of traditional remote sensing data organization and the significance of wavelet transform in texture feature extraction of remote sensing images are analyzed and based on them, a method of creating homogeneous texture feature databases and retrieval implementation by integrating Nona-tree data structure and wavelet histogram technology is presented. It can take advantage of the powerful ability of Nona-tree, which can reach balance among precision, efficiency and storage, and that of wavelet histogram, which can extract texture feature with high efficiency. Experimental results are given to prove its efficiency. Besides, this paper introduces M-band wavelet theory to texture feature representation and analyzes its advantage compared with traditional two-band wavelet transform in theory. Feature extraction and similarity calculation by adopting M-band wavelet histogram technologies are presented in detail. Retrieval performance based on 2-band wavelet histogram and M-band wavelet histogram are tested and compared.3. Thirdly, by considering the difficulty of shape-based retrieval at present and automatic man-made objects extraction synthetically, this paper explores the feasible strategy of shape-based retrieval from remote sensing images at current level. This strategy avoids the impact due to the big difficult of automatic manmade object discrimination in current state and overcomes the limits of exact matching and huge computational volume aroused by template matching. This paper studies the concrete implementation flow of contour-based retrieval based on wavelet transform modulus maxima (WTMM), multi-scale morphology and invariant relative moments. Also, experimental results are given to validate the feasibility of our strategy and the corresponding retrieval performance is analyzed.4. Fourthly, by analyzing the abstract essence of CBIR in computer science and current solution of visual feature index are analyzed. Further...