期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 109, 期 -, 页码 23-31出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2014.08.009
关键词
Band selection; Blueberry; Hyperspectral imagery; Kullback-Leibler divergence; Precision agriculture; Yield mapping
资金
- Graduate School Fellowship at the University of Florida
Hyperspectral imagery divides spectrum into many bands with. very narrow bandwidth. It is more capable to detect or classify objects, where visible information is not sufficient for the task. However, hyperspectral image contains a large amount of redundant information, which eliminates its discriminability. Band selection is used to both reduce the dimensionality of hyperspectral images and save useful bands for further application. This study explores the feasibility of hyperspectral imaging for the task of classifying blueberry fruit growth stages and background. Three information theory based band selection methods using Kullback-Leibler divergence: pair-wise class discriminability, hierarchical dimensionality reduction and non-Gaussianity measures were applied. Three classifiers, K-nearest neighbor, support vector machine and AdaBoost were used to test the performance of the selected bands by the three methods. The selected bands achieved classification accuracies of 88% and higher. Therefore, the band selection methods are very useful in reducing the volume of the hyperspectral data, and constructing a multispectral imaging system for detecting blueberry fruit maturity stages. (C) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据