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Remote Sensing Technology and Application  2021, Vol. 36 Issue (2): 431-440    DOI: 10.11873/j.issn.1004-0323.2021.2.0431
Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification
Wenjing Shao(),Weiwei Sun(),Gang Yang
Department of Geography and Spatial Information Techniques,Ningbo University,Zhejiang 315211,China
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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.

Key words:  Hyperspectral remote sensing      Texture      Classification      Feature extraction     
Received:  12 December 2019      Published:  24 May 2021
ZTFLH:  TP75  
Corresponding Authors:  Weiwei Sun     E-mail:;
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Wenjing Shao
Weiwei Sun
Gang Yang

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Wenjing Shao,Weiwei Sun,Gang Yang. Comparison of Texture Feature Extraction Methods for Hyperspectral Imagery Classification. Remote Sensing Technology and Application, 2021, 36(2): 431-440.

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Fig.1  The image of Indian Pines
Fig.2  The image of Pavia University
Fig.3  The image of Xiong'an
Table 1  Classification accuracy of Indian Pines data
Table 2  Classification accuracy of Pavia University data
Table 3  Classification accuracy of Xiong'an data
帕维亚大学5 351.0267.22100.18103.212 039.11 366.002.080.48
雄安14 798.63 020.59321.98328.467 913.02595.024.243.25
  Tabale4 Computational time of different methods
Fig.4  Classification maps of Indian Pines data
Fig.5  Classification maps of Pavia University data
Fig.6  Classification maps of Xiong'an data
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