基于支持向量机的最小噪声旋转变换高光谱遥感图像分类

Denghui Zhang, Yu Le
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引用次数: 13

摘要

利用支持向量机(SVM)作为高光谱图像分类器,从分类精度的角度分析了最小噪声分数(MNF)旋转变换的分量选择问题。利用验证点和验证图对五组不同数量的MNF组件进行了评估。进一步的评估包括分类误差分布和分离类精度比较。利用AVIRIS高光谱数据进行的实验结果表明,保留1/10左右的MNF分量可以达到最佳精度。然而,对于不同的目标类别,最优的MNF分量数是方差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support Vector Machine Based Classification for Hyperspectral Remote Sensing Images after Minimum Noise Fraction Rotation Transformation
The component selection of minimum noise fraction (MNF) rotation transformation is analyzed in terms of classification accuracy using support vector machine (SVM) as a classifier for hyper spectral image. Five different group of different number of MNF components are evaluated using validation points and validation map. Further evaluation including classification error distribution and separation-class accuracies comparison are performed. The experimental result using AVIRIS hyper spectral data shows that keep about 1/10 MNF components could achieve best accuracies. However, for different target classes, the optimal number of MNF components is variance.
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