An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery

In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to inte...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2008-12, Vol.46 (12), p.4173-4185
Main Authors: Huang, Xin, Zhang, Liangpei
Format: Article
Language:English
Subjects:
Algorithms
Applied geophysics
Bandwidth selection
Classification
Computer programs
Data mining
Derivatives
Earth sciences
Earth, ocean, space
Exact sciences and technology
Extraction
high spatial resolution
hyperspectral
Hyperspectral imaging
Hyperspectral sensors
Image analysis
Image resolution
Imagery
Internal geophysics
mean shift (MS)
Neural networks
Remote sensing
Spatial resolution
Studies
Support vector machines
Urban areas
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Summary:In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to interpret the feature set. In order to employ MS for hyperspectral data effectively, a feature-extraction algorithm, nonnegative matrix factorization, is utilized to reduce the high-dimensional feature space. Furthermore, two bandwidth-selection algorithms are proposed for the MS procedure. One is based on the local structures, and the other exploits separability analysis. Experiments are conducted on two hyperspectral data sets, the DC Mall hyperspectral digital-imagery collection experiment and the Purdue campus hyperspectral mapper images. We evaluate and compare the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely, the derivative of the morphological profile. Experimental results show that the proposed MS-based analysis system is robust and obviously outperforms the other methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2008.2002577