王凯, 舒宁, 孔祥兵, 李亮. 一种多特征转换的高光谱影像自适应分类方法[J]. 武汉大学学报 ( 信息科学版), 2015, 40(5): 612-616. DOI: 10.13203/j.whugis20130384
引用本文: 王凯, 舒宁, 孔祥兵, 李亮. 一种多特征转换的高光谱影像自适应分类方法[J]. 武汉大学学报 ( 信息科学版), 2015, 40(5): 612-616. DOI: 10.13203/j.whugis20130384
WANG Kai, SHU Ning, KONG Xiangbing, LI Liang. A Multi-feature Conversion Adaptive Classification of Hyperspectral Image[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 612-616. DOI: 10.13203/j.whugis20130384
Citation: WANG Kai, SHU Ning, KONG Xiangbing, LI Liang. A Multi-feature Conversion Adaptive Classification of Hyperspectral Image[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 612-616. DOI: 10.13203/j.whugis20130384

一种多特征转换的高光谱影像自适应分类方法

A Multi-feature Conversion Adaptive Classification of Hyperspectral Image

  • 摘要: :光谱相似性测度用来衡量像元光谱的相似程度,是高光谱影像光谱匹配分类的重要工具之一,一般通过设置阈值判断像元光谱和参考光谱是否相似来进行分类。在此基础上,本文提出了一种多特征转换的高光谱影像自适应分类方法,实现了各种光谱相似性特征和分类器相结合的一种自适应分类。实验结果表明,本文提出的方法相比于传统的SVM方法,分类的总体精度更高,还可以避免部分传统光谱匹配分类方法中需要专家经验确定分类阈值的复杂过程。

     

    Abstract: Spectral similarity measure is an important tool of hyperspectral remote sensing image clas-sification.By setting the threshold to judge the pixel spectrum and the reference spectra is similar ordissimilar.To overcome this problem,this paper proposes a multi-feature conversion adaptive classifi-cation of hyperspectral image,this is done through using spectral similarity measure value as similari-ty patterns.Experimental results show that the proposed methods are,compared with the traditionalSVM method in the overall accuracy of classification,increased by 6.25%and 8.72%,also it impliesthat using simple learnable measures outperforms complex and manually turned techniques used in tra-ditional classification.

     

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