Abstract

Hyperspectral images are efficient tools for discriminating different types of earth's surface materials. Spectral features traditionally perform classification of hyperspectral images, but different studies have proved the efficiency of spatial features as complementary information in increasing the classification accuracy. The fractal geometry can be regarded as a potent tool for spatial data modeling. This study proposes a new classification method based on the integration of fractal and spectral features. For this purpose, three groups of fractal features, including mono-fractal, lacunarity and multi-fractal features are generated from the first few principal components of the hyperspectral image in different window sizes. These features are later stacked with spectral features and then fed to support vector machines classifier. The experiments are conducted on two real hyperspectral images Indian Pines, Pavia University. Final classification accuracy proved the superiority of the proposed classification method against the other competitive spatial-spectral methods.

摘要

高光谱图像是区分不同类型地球表面材料的有效工具。光谱特征传统上对高光谱图像进行分类，但不同的研究证明了空间特征作为补充信息在提高分类精度方面的效率。分形几何可被视为空间数据建模的有力工具。本研究提出了一种新的基于分形和光谱特征融合的分类方法。为此，从不同窗口大小的高光谱图像的前几个主成分生成三组分形特征，包括单分形、空隙和多重分形特征。这些特征随后与光谱特征堆叠在一起，然后馈送到支持向量机分类器。实验是在帕维亚大学的两个真实高光谱图像印度松树上进行的。最终分类精度证明了所提出的分类方法相对于其他竞争性空间光谱方法的优越性。