一种结合波段分组特征和形态学特征的高光谱图像分类方法

如何准确识别图像中的类别信息,是计算机视觉和模式识别领域的重要研究问题。遥感卫星图像数据,尤其是高光谱等遥感图像数据的出现,将空间信息与光谱信息集成于同一数据集中,丰富了图像信息来源。如何准确地识别高光谱图像中的地物类别,已经成为了图像处理和模式识别领域的热点问题。面向高光谱图像数据提出了一种基于波段分组特征和形态学特征的高光谱图像分类方法,结合空间和光谱特征提高分类精度。通过真实的高光谱数据实验证明:利用波段分组可以有效地保持光谱特征,降低数据冗余;在波段分组基础上结合形态学特征进行分类,比传统分类方法的分类精度明显提高。

Band Grouping Based Hyperspectral Image Classification Using Mathematical Morphology and Support Vector Machines

How to analysis and recognize image accurately is an important issue in computer vision and pattern recognition fields.Remote sensing image,especially hyperspectral images combine spatial and spectral information in one data cube.In this paper,we proposed a band grouping feature selection method,then extracted morphology features.A feature selection algorithm called recursive feature elimination was applied to decrease the dimensionality of the input morphology features data.A support vector machine was used for the final classification.Experiments performed on real hyperspectral images,confirm that it is efficient using band grouping and mathematical morphology.