COMBINER OF CLASSIFIERS USING GENETIC ALGORITHM FOR CLASSIFICATION OF REMOTE SENSED HYPERSPECTRAL IMAGES

被引:5
作者
Santos, A. B. [1 ]
Araujo, A. de A. [1 ]
Menotti, D.
机构
[1] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil
来源
2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年
关键词
Ensemble of classifiers; conscious combiners; hyperspectral images; classification; genetic algorithm;
D O I
10.1109/IGARSS.2012.6351699
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the past few years, hyperspectral images have been considered as one of the most important tool in land cover classification due to its capability to obtain rich information of materials on earth surface. In this work we aim to produce an accurate thematic map for the remote sensed hyperspectral image classification problem, which is obtained using a combination of several classification methods. Three types of feature representation and two learning algorithms (Support Vector Machines (SVM) and Backpropagation Multilayer Perceptron Neural Network (MLP)) were used yielding six classification methods to perform the combination. Our combination proposal is based on Weighted Linear Combination (WLC), in which weights are found using a Genetic Algorithm (GA) - WLC-GA. Experiments were carried out with two well-known datasets: Indian Pines and Pavia University, and we observed that our proposed WLC-GA method achieves the highest accuracy among traditional Conscious Combiners, the widely used Majority Vote (MV) and Weighted Majority Vote (WMV), for both datasets.
引用
收藏
页码:4146 / 4149
页数:4
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