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

【作者】              刘凯                                                                                                                                           

【导师】              张立福

【 学位年度 】    2013

【论文级别】      硕士    

【关键词】          高光谱数据库； 数据存储模型；  FISS ； 多维数据库 

【Key words】    Hyperspectral database, Data storage model, FISS，Multidimensional database

【中文摘要】

随着航空、航天遥感器技术以及地面成像光谱技术的迅速发展，高光谱遥感影像数据获取越加频繁，数据量也与日俱增。如何有效的管理和共享海量高光谱数据，是遥感科学工作者面临的一个重要挑战。本文以自主研发的地面成像光谱辐射测量系统（Field Imaging Spectrometer System,FISS）所采集到的高光谱数据为研究对象和主要数据来源，以建立高光谱数据库管理系统为目的，主要完成了以下两个工作:一是对高光谱数据库管理系统进行了总体设计，研究了FISS高光谱数据的存取技术以及光谱的显示方法，并结合多维数据库的理论实现了基于三维球形系统的高光谱数据查询检索和可视化，在这些研究的基础上提出了一种适合高光谱数据的管理格式—*.mdh（ Multi-dimensional hyperspectral）；二是开发了高光谱数据库管理系统，该系统集成了一些简单的光谱分析方法和基于影像数据的信息提取方法，并提出了一种基于权重光分析的光谱解混方法。

本文主要的成果和结论如下：

1、在对数据库的功能需求和总体设计进行深入分析后，根据FISS高光谱数据的特点和系统应用的需求，并以多维数据库理论为基础，设计了以GeoRaster对象为核心的高光谱数据存储模型，以此存储模型为基础进行了数据库的物理设计和详细设计，研究表明，该存储模型能够为高光谱数据库系统的数据存储、显示以及光谱分析功能提供有效的支持。

2、结合FISS数据存储模型和多维数据库理论，利用基于存储过程的数据入库方法和基于动态数据表转换的数据获取方法，实现了高光谱数据的存储和读取。在此基础上提出了一种适合高光谱数据管理的数据结构—*.mdh,该数据结构可以实现多源数据的存储、检索和三维球形系统可视化，有效的体现高光谱数据图谱合一的特性。

3、开发了以FISS数据为基础数据源的高光谱数据库管理系统，实现了数据的导入、查询、可视化功能。利用该系统尝试性的构建了FISS样本库，并以此为基础集成了简单的光谱分析功能和高光谱信息提取模块，最后提出了一种基于权重分析的光谱解混方法，为进一步的数据筛选提供了支持。

【Abstract】

With the rapid development of spaceborne and airborne sensor and ground imaging spectroscopy, hyperspectral remote sensing image data acquisition has become increasingly frequent and the amount of data is also increasing. How to effectively manage and share a mass of spectral data is a key challenge to the remote sensing scientists. In this paper, hyperspectral data collected by the self-developed ground imaging spectral radiation measurement system (Field Imaging Spectrometer System, FISS) is the main data source to be studyed. In order to establish a  spectral database system, the following two things are should to be carried out: First, the overall design of the hyperspectral database, FISS hyperspectral data access technology as well as the display of the spectrum. Combined these with the theory of multi-dimensional database based on three-dimensional spherical system of , we also come up with hyperspectral data query, retrieval and visualization. On the basis of these studies, a data format called *.mdh is proposed for hyperspectral management, and a hyperspectral database system integrated some simple spectral analysis methods and information extraction methods based on image data is devloped. Finally, a spectrum mixing analysis based on weight was presented on this paper.

In this paper, the main results and conclusions are as follows:

1. According to the characteristics of the FISS hyperspectral data and system application requirements, we designed the Hyperspectral data storage model, in which GeoRaster is as the core object. With this storage model, physical database design and detailed design is also been carried out. Research have showed that this model is useful to the the storage and display of hyperspectral database system data, and provides effective support for spectral analysis.

2. Combined with the FISS data storage model, the data storage methods based on the stored procedure and the data acquisition method based on dynamic data table conversion is used to implement the hyperspectral data storage and read. On the basis of these data structure, we proposed a new fomat for hyperspectral data management – *.mdh. This data structure can be used for multi-source data storage, retrieval and three-dimensional spherical system visualization, which is the manifestation of hyperspectral data’s spectral and image.

3. The hyperspectral database system, in which the Fiss data is the main data source is deveoped. This system have implemented the data import, query and visualization. Using the database system we attempt to built a FISS sample database and integrate some simple spectral analysis method and hyperspectral information extraction module in it. Finally, a weight spectral unmixing method based on a FISS spectrum sample library is proposed, and it is supportive for further data filtering.