ABSTRACT

This paper introduces a new object-based spectral-spatial classification method for hyperspectral image. The kernel principal component analysis (KPCA) is firstly performed over subspaces (KPCAsub) derived from the original spectral domain, which incorporates linear information with nonlinear formulation. The obtained image is then processed via a feature-level fusion with superpixel segmentation at different scales. The final classification result is achieved by a cross-scale superpixel based (CSSP) decision fusion framework based on each individual operation of support vector machine. The resulting method, called KPCAsub-CSSP, contributes to better characterization under-limited sample condition, and promotes spectral-spatial integration in terms of echoing the complex distribution of ground objects. The experimental results on two real hyperspectral data sets demonstrate that the proposed method exhibits good performance in comparison to the other related methods.

摘要

本文介绍了一种新的基于对象的高光谱图像光谱空间分类方法。内核主成分分析（KPCA）首先在从原始光谱域派生的子空间（KPCAsub）上执行，该子空间将线性信息与非线性公式结合在一起。然后，通过具有不同比例的超像素分割的特征级融合来处理获得的图像。最终的分类结果是通过基于支持向量机的每个单独操作的跨尺度超像素（CSSP）决策融合框架来实现的。所得方法称为KPCAsub-CSSP，有助于在有限的样品条件下更好地表征，并在回波地面物体的复杂分布方面促进光谱空间整合。