曲海成,郭月,王媛媛

• 1:辽宁工程技术大学软件学院

摘要(Abstract)：

针对现有高光谱图像分类方法对空间和谱间信息的利用不充分,限制了地物分类的准确度的问题,该文提出一种基于正交线性判别分析和三维离散小波变换的高光谱图像空谱联合分类算法。该算法首先利用正交线判别分析对高光谱图像进行特征提取和特征缩减;然后将提取后的特征经三维离散小波从不同尺度、频率和方向上分解,以级联的方式获得具有正交类判别信息的空谱融合特征集;最后,将空谱融合特征集作为概率支持向量机的输入特征,分类的结果再通过马尔可夫随机场利用空间上下文信息细化分类图,进一步提升分类准确度。在Indian Pines和Salinas两组数据集上的实验表明,相比其他算法,该算法能达到更高的总体分类准确度和Kappa系数,并且在大部分的地物类别上的分类准确度有着较为明显地提升。

关键词(KeyWords)： 正交线性判别分析;三维离散小波变换;马尔可夫随机场;分类

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金青年基金项目(41701479);; 生产技术问题创新研究基金项目(20160092T)

作者(Author): 曲海成,郭月,王媛媛

DOI: 10.16251/j.cnki.1009-2307.2019.08.012

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