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Proceedings Paper

Statistical modeling for improved land cover classification
Author(s): Yunxin Zhao; Xiaobo Zhou; Kannappan Palaniappan; Xinhua Zhuang
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Paper Abstract

Novel statistical modeling and training techniques are proposed for improving classification accuracy of land cover data acquired by LandSat Thermatic Mapper (TM). The proposed modeling techniques consist of joint modeling of spectral feature distributions among neighboring pixels and partial modeling of spectral correlations across TM sensor bands with a set of semi-tied covariance matrices in Gaussian mixture densities (GMD). The GMD parameters and semi-tied transformation matrices are first estimated by an iterative maximum likelihood estimation algorithm of Expectation- Maximization, and the parameters are next tuned by a minimum classification error training algorithm to enhance the discriminative power of the statistical classifiers. Compared with a previously proposed single-pixel based Gaussian mixture density classifier, the proposed techniques significantly improved the overall classification accuracy on eight land cover classes from imagery data of Missouri state.

Paper Details

Date Published: 6 August 2002
PDF: 9 pages
Proc. SPIE 4741, Battlespace Digitization and Network-Centric Warfare II, (6 August 2002); doi: 10.1117/12.478725
Show Author Affiliations
Yunxin Zhao, Univ. of Missouri/Columbia (United States)
Xiaobo Zhou, Univ. of Missouri/Columbia (United States)
Kannappan Palaniappan, Univ. of Missouri/Columbia (United States)
Xinhua Zhuang, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 4741:
Battlespace Digitization and Network-Centric Warfare II
Raja Suresh; William E. Roper; William E. Roper, Editor(s)

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