高光谱遥感影像核最小噪声分离变换特征提取

Hyperspectral Image Feature Extraction via Kernel Minimum Noise Fraction Transform

针对高光谱遥感影像线性特征提取方法易导致信息丢失和失真的特点,在最小噪声分离(MNF)变换的基础上,引入核方法,提出核最小噪声分离(KMNF)变换高光谱影像非线性特征提取方法。Cuprite矿区AVIRIS数据实验结果表明,样本个数对KMNF特征提取的结果影响很小,较少的样本即可达到较多样本时特征提取的效果;KMNF特征提取体现了高光谱影像的非线性特征,KMNF特征提取后的影像可获得优于MNF特征提取的端元提取效果。

Hyperspectral image linear feature extraction methods often cause information loss and distortion. In view of this, a new kernel minimum noise fraction（KMNF） transform hy- perspectral image nonlinear feature extraction method is proposed that introduces a kernel method to minimum noise fraction（MNF） transform. Hyperspectral image KMNF feature extraction experiments were carried out. CUPRITE AVIRIS data experimental results show that sample number influences KMNF slightly, a small number of samples can get almost the same result as a large number of samples~ KMNF feature extraction reflects the nonlinear characteristics of hyperspectral images, and endmember extraction effects based on KMNF images outweigh MNF images.