A multiple classifier system based on Ant-Colony Optimization for Hyperspectral image classification

2016 International Conference on Communication, Image and Signal Processing

物理学;无线电电子学;计算机科学

Tang, Ke^1 ; Xie, Li^1 ; Li, Guangyao^1

Tongji Universirt, No 4800, Cao'an Road, Shanghai, China^1

关键词: Ant Colony Optimization algorithms;    Classification ability;    Classification algorithm;    Classification methods;    K nearest neighbours (k-NN);    Multiple classifier systems;    Multispectral images;    Statistical classifier;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/787/1/012011/pdf
DOI  :  10.1088/1742-6596/787/1/012011

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Hyperspectral images which hold a large quantity of land information enables image classification. Traditional classification methods usually works on multispectral images. However, the high dimensionality in feature space influences the accuracy while using these classification algorithms, such as statistical classifiers or decision trees. This paper proposes a multiple classifier system (MCS) based on ant colony optimization (ACO) algorithm to improve the classification ability. ACO method has been implemented on multispectral images in researches, but seldom to hyperspectral images. In order to overcome the limitation of ACO method on dealing with high dimensionality, MCS is introduced to combine the outputs of each single ACO classifier based on the credibility of rules. Mutual information is applied to discretizing features from the data set and provides the criterion of band selection and band grouping algorithms. The performance of the proposed method is validated with ROSIS Pavia data set, and compared to k-nearest neighbour (KNN) algorithm. Experimental results prove that the proposed method is feasible to classify hyperspectral images.