%A Wenjing Shao,Weiwei Sun,Gang Yang %T Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification %0 Journal Article %D 2021 %J Remote Sensing Technology and Application %R 10.11873/j.issn.1004-0323.2021.2.0431 %P 431-440 %V 36 %N 2 %U {http://www.rsta.ac.cn/CN/abstract/article_3355.shtml} %8 2021-04-20 %X

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.