引用本文: 白璘, 刘盼芝, 惠萌. 利用小波核最小噪声分离进行高光谱影像SVM分类[J]. 武汉大学学报 ( 信息科学版), 2016, 41(5): 624-628,644.
BAI Lin, LIU Panzhi, HUI Meng. SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 624-628,644.
 Citation: BAI Lin, LIU Panzhi, HUI Meng. SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 624-628,644.

## SVM Classification of Hyperspectral Image Based on Wavelet Kernel Minimum Noise Fraction

• 摘要: 针对高光谱遥感影像线性特征提取方法在一定程度上会降低地物类别的可分性问题,在最小噪声分离变换基础上引入核方法,以小波核函数代替传统核函数,并将新型核最小噪声分离方法与支持向量机方法相结合,对高光谱影像数据进行分类。实验结果表明,基于小波核最小噪声分离变换的方法适合于高光谱遥感影像的非线性特征,将其应用于HYDICE系统与AVIRIS系统所获得的实验数据集,与对照算法相比,总体分类精度可提高3%~9%。

Abstract: Linear features extraction methods for hyperspectral imaging reduce feature class separability. Aiming to solve these problems, this paper introduces a novel kernel method based on minimum noise fraction transformation. This method focuses on the kernel function in the minimum noise fraction transformation and replacing the traditional kernel function with a wavelet kernel function. This new method improves the nonlinear mapping capability of kernel minimum noise fraction transformation by exploiting the features of multi-resolution analysis. Classification experiments on hyperspectral image data combined the novel kernel minimum noise fraction transformation and support vector machine; simulation results show that the wavelet kernel minimum noise fraction transformation method is suitable for the nonlinear characteristics of hyperspectral images. The proposed method was applied to HYDICE and AVIRIS data, and compared with other algorithms. Classification accuracy increased 3%-9%.

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