NDLI: Multifractal based hyperion hyperspectral data mining

Content Provider	IEEE Xplore Digital Library
Author	Zhou Ziyong
Copyright Year	2010
Description	Author affiliation: State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum, Beijing, China, 102249 (Zhou Ziyong)
Abstract	Traditional methods effectively used for processing multispectral data is usually limited to deal with hyperspectral images, which are characterized by massive data and higher spectral resolution. In this paper, multifractal is introduced to mining hyperspectral data by analyzing the holistic feature of spectral curve, and blanket method is adopted to compute fractal dimension (fractal signature) of each spectral curve within Hyperion image. The experimental result shows the advantage of fractal signature over original spectral to identify the objects, and the down fractal signature may be more effective to discriminate objects than up fractal signature for the Hyperion data. The experimental result also indicates that the initial scale affects the fractal signature value, and the number of bands affects both fractal signature value and feature scale. The primary result implies that the multifractal based method may be a feasible approach to mining hyperspectral data.
Starting Page	2109
Ending Page	2113
File Size	409395
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	data mining Hyperspectral Fractals Surface topography Data mining Pixel Hyperspectral imaging Multifractal
Content Type	Text
Resource Type	Article

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