Statistical modeling for improved land cover classification

Yunxin Zhao; Xiaobo Zhou; Kannappan Palaniappan; Xinhua Zhuang

doi:10.1117/12.478725

6 August 2002 Statistical modeling for improved land cover classification

Yunxin Zhao, Xiaobo Zhou, Kannappan Palaniappan, Xinhua Zhuang

Proceedings Volume 4741, Battlespace Digitization and Network-Centric Warfare II; (2002) https://doi.org/10.1117/12.478725
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Yunxin Zhao, Xiaobo Zhou, Kannappan Palaniappan, and Xinhua Zhuang "Statistical modeling for improved land cover classification", Proc. SPIE 4741, Battlespace Digitization and Network-Centric Warfare II, (6 August 2002); https://doi.org/10.1117/12.478725

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KEYWORDS

Statistical analysis

Data modeling

Matrices

Expectation maximization algorithms

Error analysis

Statistical modeling

Spectral models

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