考虑关联波段特性的光谱相似图像分类方法
A Spectral similar image classification method considering correlated band characteristics
周文芳 1杨耀宁2
作者信息
- 1. 长江大学文理学院,湖北荆州 434020
- 2. 云南大学建筑与规划学院,昆明 650500;柏林工业大学,德国柏林10623
- 折叠
摘要
光谱相似图像分类性能过差会增加光谱信息冗余度,降低地物勘探与军事防御等多种领域的光谱探测效率.为了多元素匀质区分光谱信息与光谱曲线,提出考虑关联波段特性的光谱相似图像分类方法.该方法首先利用光谱匹配消除光谱相似图像白色光源过曝现象.然后提取优化图像的关联波段,并将其作为聚类特征输入支持向量机中.最后根据支持向量机的输出结果,实现光谱相似图像分类.实验结果表明,所提方法分类结果清晰度较高,分类误差或像素块填色错误小,混淆矩阵中同行同列矩形块的分类精度较高.
Abstract
The poor classification performance of spectrally similar images will increase the redundancy of spectral information and reduce the spectral detection efficiency in various fields such as ground feature exploration and military defense.In order to distinguish spectral information and spectral curves homogeneously with multiple elements,a spec-tral similarity image classification method considering the characteristics of associated bands is proposed.The method first uses spectral matching to eliminate overexposure of white light sources in spectrally similar images.The associated bands of the optimized image are then extracted and fed into the support vector machine as clustering features.Finally,according to the output results of the support vector machine,the spectral similarity image classification is realized.The experimental results show that the classification results of the proposed method have high definition,small classifi-cation errors or pixel block coloring errors,and the classification accuracy of rectangular blocks in the same row in the confusion matrix is high.
关键词
光谱相似图像/光谱匹配/关联波段/聚类特征/支持向量机Key words
spectral similarity images/spectral matching/associated bands/clustering features/support vector machines引用本文复制引用
基金项目
云南省青年科学基金(202001BB050070)
出版年
2024
NETL
NSTL
万方数据