KEYWORDS: Databases, Data mining, Analytical research, Remote sensing, Biological research, Mining, Relativity, Data conversion, Data processing, Data integration
Remote sensing data, especially the hyperspectral remote sensing data, characterize their great quantities. So how to deal wtih these data is a focus. Database has solved the problem of storing, searching, updating and maintaining of the data, but it is not satisfactory in disposing them. In recent years, the technology of data warehouse has great development. It can re-integrate, synthesize and separate the data of database, and use the searching pattern of multiple dimensions to realize data mining (DM). This technolgoy has been widely used in commerce to analyze the inner relationship of the numerous data and makes some remarkable achievements in decision supporting. Data warehouse and Data mining technology have been used in GIS. This article would give a set of complete steps and some general methods in using the DM to analyze the remote sensing data, especially in hyperspectral data. And it tries to do some preliminary exploration in using it to deeply analyze the potential relations among the acquired spectra, images and biology parameters of the experiments and get some anticipated possible results.
In the hyperspectral remote sensing, the continuum-removed method is used only with the spectrum of a single pixel to analyze spectrum and extract the feature bands useful with the classification. While in this paper, based on the continuum-removed algorithm, we programmed with Visual C++ to fulfill the functions of the continuum removed to the whole hyperspectral image, normalizing and extracting the feature space for the classification. At last, aiming at the former image and the after-continuum removed image, the classification results of the MLC and SAM are compared.
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