贝叶斯方法和非参数模型支持下的遥感影像线性光谱分解

doi:10.3969/j.issn.0255-8297.2016.06.002

Abstract

Abstract:

To solve the problem of spectral uncertainty in spectral unmixing of remote sensing images, a non-parametric model is proposed to describe the probability distribution of land cover spectrum. A Bayesian method is used to derive a posteriori probability distribution of the proportion of land cover types. Proportions of land cover types are calculated using unbiased estimation and maximum likelihood estimation. Experiment is carried out to map the impervious surface using a Landsat image. The results show that the proposed Bayesian method has higher accuracy than conventional linear spectral unmixing algorithms and the method of hard classification, therefore is effective in solving the spectral uncertainty problem.

Key words: remote sensing, mixed pixel, non-parametric model, end-member, Bayes

CLC Number:

P751.1

LI Xi, CHEN Feng-rui, LI Chang. Linear Spectral Unmixing of Remote Sensing Images Using Bayesian Method and Non-parametric Model[J]. Journal of Applied Sciences, 2016, 34(6): 661-669.

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Linear Spectral Unmixing of Remote Sensing Images Using Bayesian Method and Non-parametric Model

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