一种基于变权重组合的光谱相似性测度

A Spectral Similarity Measure Based on Changing-Weight Combination Method

光谱相似性测度是高光谱遥感影像信息提取的关键。在欧氏距离和光谱角余弦的基础之上提出一种变权重组合的光谱相似性测度,即光谱变化权重相似性测度。这种光谱相似性测度可根据不同地物类别自动对欧氏距离和光谱角余弦测度指标配比权重。选用标准光谱库和机载OMIS高光谱影像对SCWM进行测试,并引入误分率和混淆矩阵对分类结果进行评价。结果表明,相对于仅采用一种或两种光谱相似性测度的分类方法,光谱变化权重相似性测度具有更精细的光谱识别能力。

The spectral similarity measure is the key to extract the information from hyperspectral remote sensing imagery, A new changing-weight spectral similarity, called spectral changing-weight similarity measure （SCWM）, was proposed based on the combination of Euclidean distance and spectral angle cosine, According to different land covers, SCWM can automatically alter the weight of Euclidean distance and spectral angle cosine, The classi- fication accuracy of different spectral similarity measures was compared by using the misclassification rate of the standard spectral library data and the confusion matrix of the airborne OMIS hyperspectral image, The experimental results demonstrate that spectral changing-weight similarity measure is more effective than the spectral similarity measure by taking into account one spectral feature or two spectral features to the precise classification.