This paper presents a method for hyperspectral image classification using support vector data description (SVDD) with Gaussian kernel function. SVDD has been a popular machine-learning technique for single-class classification, but selecting the proper Gaussian kernel bandwidth to achieve the best classification performance is always a challenging problem. In this paper, we propose a new automatic, unsupervised Gaussian kernel bandwidth selection approach. A multi-class SVDD classification scheme is designed based on the proposed approach and applied to hyperspectral image data. The performance of the multi-class SVDD classification scheme is evaluated on three frequently used hyperspectral data sets and preliminary results show our proposed method can achieve better performance than published results on these data sets.

翻译：本文介绍了一种使用支持矢量数据描述(SVDD)的超光谱图像分类方法,该方法具有高森内核功能。 SVDD一直是用于单级分类的一种流行的机器学习技术,但选择适当的高森内核带宽以实现最佳分类性能总是一个具有挑战性的问题。在本文件中,我们提出了一个新的自动、不受监督的高斯内核带宽选择方法。一个多级SVDD分类方案是根据拟议方法设计的,并应用于超光谱图像数据。多级SVDD分类方案的性能对三种常用的超光谱数据集进行了评估,初步结果显示,我们拟议的方法的性能可以比这些数据集上公布的结果更好。