高光谱遥感影像纹理特征提取的对比分析

地物的"同物异谱"或"异物同谱"问题,使得仅仅依据高光谱影像的光谱信息较难得到理想的分类精度.纹理特征是地物空间分布的重要结构信息,能够一定程度上弥补光谱特征在高光谱遥感影像分类中的不足.纹理特征提取在高光谱遥感影像分类中得到了诸多发展,然而当前的纹理特征方法缺乏较为全面的对比分析.因此,选取旋转不变局部二值模式、简单...

Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification

The problem of “same object with different spectrum” and “different objects with same spectrum” makes that it difficult to obtain high classification accuracy for hyperspectral images using the single spectral information. Texture feature is the important structural information of spatial distribution of ground objects， which can compensate for the deficiency of spectral features in the classification to some extent. Many texture feature extraction methods have been developed in hyperspectral image classification， but they are lacking of a comprehensive comparative analysis. Therefore， this paper aim to explore the classification performance of different texture feature extraction methods. The 8 selected methods include rotational invariant local binary mode （riLBP）， Simple Linear Iteration （SLIC）， Extended Morphological Profile （EMP）， Differential Morphological Profile （DMP）， Attribute Profile （AP）， 3D-Gabor， Joint Bilateral Filtering （JBF） and Guided Filtering （GF） design classification experiments. Experimental results on Indiana Pines， Pavia University and Xiong'an datasets show that EMP behaves better than other methods both in overall classification accuracy and computational speeds.