中国科学院遥感与数字地球研究所-高光谱遥感应用技术研究室推荐内容
【2006博】高光谱数据库及数据挖掘研究
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【作者】 李兴 【导师】 童庆禧；郑兰芬；张兵 【 学位年度 】 2006 【论文级别】 博士 【关键词】 植被指数时间序列，傅立叶谐波分析，HANTS，频数，异常值检测 【Key words】 the vegetation index time series of remotely sensed data, Harmonic Analysis, HANTS, number of frequency, outlier detection 【中文摘要】 高光谱遥感技术是指具有10-2λ的光谱分辨率，在可见光到短波红外波段其光谱分辨率高达纳米数量级的遥感技术。随着航空、航天遥感器技术的迅速发展和空间信息需求的日益扩大，遥感影像数据获取越加频繁，数据量也与日俱增。高光谱遥感影像更是因为其数据量巨大而对影像数据库的发展提出了新的挑战。在海量的光谱数据和影像数据被数据库有效的管理起来之后，从这些TB 级的数据中挖掘有用的信息，成为了高光谱遥感应用的主要研究方向之一。这些数据并不完全结构化，数据质量也不尽完好，因此数据挖掘领域的相关技术方法被应用到高光谱遥感领域，在数据的海洋之中萃取知识的精华。 本篇论文在综合描述了高光谱遥感技术和数据库技术的发展背景前提下，提出了高光谱数据库和光谱数据挖掘的内涵和外延。并以此为切入点，对国内外地面光谱数据库、遥感影像数据库的发展和研究现状、数据挖掘技术的发展和研究现状，以及空间数据挖掘和影像数据挖掘技术做了深入探讨。通过对国内外研究进展的把握和自身项目研究知识积累，针对数据挖掘和应用方向提出了高光谱数据库的系统设计，并通过导入了六千多条数据，在ORACLE 平台上完成了技术实现。以建立的高光谱数据库为基础，对光谱数据挖掘应用和高光谱影像数据挖掘应用做了一些开创性的研究工作。本文取得的研究进展和创新点归纳如下： 1. 在构建高光谱数据库基础之上，提出了针对数据库存储方案的光谱数据模型和影像数据模型，通过将必要的高光谱分析方法和分析模型整合到数据库之中，实现了数据、方法、模型在高光谱数据库中的集成与统一。 2. 将多源数据统一到一个数据库平台之中，将原有的大表结构和表群结构作转换，设计了以二元数据为核心的星型数据库概念结构，建立了通用的高光谱数据库建库模式和模型设计线路。 3. 将数据库数据挖掘技术应用在光谱数据模拟和光谱参量分析研究上，从光谱数据和属性数据这两个方向在数据库中实现了高光谱数据挖掘的应用。 4. 将高光谱影像数据空间映射到数据库表空间，从而实现借助于数据库平台对高光谱影像进行数据分析与信息挖掘，实现了非负矩阵分解的逆变换高光谱数据压缩、最小长度模型辅助高光谱影像波段选择、非负矩阵分解高光谱影像特征提取、支持向量机的高光谱图像目标提取等高光谱数据自动分析模型。
【Abstract】 Hyperspectral remote sensing is a cutting-edge technology of acquiring land surface information, with a high resolution in spectrum. With the fast development of spaceborne and airborne sensors, hyperspectral data are more frequently and conveniently received. As a result, data storage, management and effective information extraction are becoming key challenges to hyperspectral remote sensing science and technology. Especially, automatic or semi-automatic information extraction is a predictabley trend for hyperspectral researches and applications. In this dissertation, the intension and extention of the concept hyperspectral database and spectral data mining has been brought forward after a background research on hyperspectral remote sensing and database. Based on this conception, ground-object spectra database, remote sensing image database, data mining technology have been studied, while spatial data mining and image data mining technology are focused. A conceptual design with data mining and application oriented is provided basetd upon project experience and international study. The hyperspectral database has been built up and more than 6,000 sets of spectral data have been uploaded. There are spectra of rocks, minerals, waters, concretes, trees, wheats, soils and hyperspectral images in this database. With this hyperspectral database, some researches on spectral data mining and hyperspectral image data mining have been carried out. In view of hyperspectral remote sensing technology and application, there are some advangates of this dissertation as follows: 1. Based on hyperspectral database, new storage models of spectra and hyperspectral image has been put forward. A new design has combined data, methods and models in a whole database platform. 2. To integrage different sources of spectra in one database platform, a new conceptual data structure, dualistic-core star structure, has been brought forward. 3. Forward (from spectra to attributes) and backward researches have been impleted based on hyperspectral database. Automatic optimizations have been applied in band combination and band selection. 4. From a totally different view of hyperspectral image, a projection from hyperspectral images to relation tables has been built up to improve analysis and information extraction. The following key techniques are implemented in hyeprspectral database: Band selection based on Minium Length Model, Feature extraction based on Non-negative Matrix Fraction, Data compression and decompression based on Non-negative Matrix Fraction, Object Decection based on Support Vector Machine.
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