OPTIMIZATION OF GAUSSIAN MIXTURE MODEL USING GENETIC ALGORITHM FOR LAND COVER CLASSIFICATION

Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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ARCHITECTUREUR01_037

تاریخ نمایه سازی: 9 مرداد 1395

Abstract:

Gaussian Mixture Models (GMMs) have been frequently applied in hyperspectral image classification tasks. The problem of estimating the parameters in a Gaussian mixture model has been studied in the literature. Expectation-Maximization (EM) algorithm is one of the methods that can be applied for this problem. EM is a general method for optimizing likelihood functions and is useful in situations where data might be missing or simpler optimization methods fail [1]. On the other hand, the large number of bands in a hyperspectral images leads into estimation of a large number of parameters. In this paper, we use the Genetic Algorithm (GA) for solving the high dimensionality of the data and optimizing the EM-GMM classifier. In order to evaluate the proposed algorithm in real analysis scenarios, we use two benchmark hyperspectral data sets collected by AVIRIS and Reflective Optics System Spectrographic Imaging System (ROSIS).

Authors

h ghanbari

Dept. of Geomatics, College of Engineering, U. of Tehran, Iran

s homayouni

Dept. of Geography, Environmental Studies and Geomatics, U. of Ottawa, Ottawa, Canada

a safari

Dept. of Geomatics, College of Engineering, U. of Tehran, Iran

a mohammadpour

Dept. of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

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