基于小波分量特征值匹配的高光谱影像分类
Hyperspectral Image Classification Based on Features of Wavelet Vectors
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摘要: 提出了一种基于小波分量特征值的高光谱影像分类算法。针对每个像素构建一个能反映该分量特征的函数,得到其特征值。再利用这些特征值与参考光谱的特征值进行匹配,从而对整幅影像实现分类。实验证明,该方法比传统的光谱角制图法和交叉相关系数法的分类精度有较大提高。Abstract: A novel approach for mapping hyperspectral data is presented by extracting the feature of wavelet vectors.The expected decomposed level-L is decided for a given hyperspectral imagery and then a function is constructed to extract the feature of each vector for every pixel.Classified image is obtained by comparing the feature between the pixel vector and the reference spectra.Comparisons with spectral angle mapping(SAM) and cross correlation spectral match(CCSM) are also done.
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