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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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Abstract  

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:  1811073014@nbu.edu.cn;sunweiwei@nbu.edu.cn
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Wenjing Shao
Weiwei Sun
Gang Yang

Cite this article: 

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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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2021.2.0431     OR     http://www.rsta.ac.cn/EN/Y2021/V36/I2/431

Fig.1  The image of Indian Pines
Fig.2  The image of Pavia University
Fig.3  The image of Xiong'an
地物类别纹理特征提取方法/%
riLBPSLICEMPDMPAP3D-GaborJBFGFSVM
苜蓿95.1284.8897.5698.5494.0229.0262.4429.7628.78
玉米未耕地98.3991.4697.3296.7695.9862.3782.1179.8155.83
玉米疏耕地98.8492.6498.5398.2697.0760.1364.3163.2747.20
玉米98.7582.8296.9099.0694.3849.3084.8463.9931.46
牧草地98.0193.6898.2197.2096.7787.3893.5694.1683.52
草树混合地99.3497.6399.8899.4899.0898.22100.00100.0090.91
修剪的牧草93.3395.2799.2097.6096.5374.0094.0096.0056.80
干牧草99.8498.51100.00100.0099.5997.56100.00100.0093.93
燕麦98.1577.22100.00100.0093.8426.678.331.1126.67
大豆未耕地98.7492.9497.9297.5396.7862.8286.1376.4658.49
大豆疏耕地99.3795.4599.6199.3398.4478.3795.2795.5078.79
大豆已耕地98.0084.1098.1397.4594.4247.7572.9068.1640.37
小麦100.0093.4899.1399.4698.0296.52100.0099.8993.04
树木99.8598.32100.0099.9899.5495.4099.8999.9394.39
林间小道99.1491.87100.0099.8397.7155.8860.7268.2732.02
钢铁塔94.8489.1796.43100.0095.1186.5593.8199.6480.12
OA98.9894.0298.8898.6494.2374.8488.0386.2369.66
AA98.1191.5498.6898.7896.7069.2581.1477.2562.02
KC98.8393.1798.7298.4593.4271.1269.7065.2665.08
Table 1  Classification accuracy of Indian Pines data
地物类别纹理特征提取方法/%
riLBPSLICEMPDMPAP3D-GaborJBFGFSVM
柏油路91.4595.2496.6392.1897.9691.1897.8296.7388.69
草地99.2599.5199.8599.4599.6895.9299.8110096.05
沙砾94.6377.3198.4488.4794.5264.9973.6365.4247.16
树木60.1690.5197.3095.3093.8190.3589.8483.6875.35
金属板93.1898.3599.8696.5398.6398.6510010099.78
裸地98.6999.1699.9398.4398.4974.8579.1571.6753.72
沥青屋顶87.5690.8795.0894.7699.7171.1675.6377.7159.18
地砖95.4188.3795.0692.7698.0183.2697.7898.4479.34
阴影45.1890.3089.9182.0396.6595.4898.6299.7780.91
OA92.8795.6098.3396.2098.3689.0192.8893.0483.30
AA85.0692.1896.9093.3297.5085.0990.2588.1675.76
KC90.4994.1697.7894.9497.8285.3181.5580.7777.45
Table 2  Classification accuracy of Pavia University data
地物类别纹理特征提取方法
riLBPSLICEMPDMPAP3D-GaborJBFGFSVM
复叶槭97.7698.5999.5496.8697.7991.7295.3396.1989.34
柳树93.7897.4699.9095.2798.8379.6563.4961.1175.31
房屋97.4398.8098.4796.7496.6595.1996.5997.0892.93
桃树94.1795.3197.1386.7888.8172.9784.2484.5868.11
国槐95.6897.1698.9293.8495.6687.1790.9892.2985.33
白腊梅98.8698.7599.5398.6399.1795.6699.3899.8493.98
草地89.8282.2492.7981.8476.3964.6156.5153.1755.55
水域99.0898.5299.3197.3997.9695.3699.1999.9294.71
稀疏林92.0081.3169.9347.8327.1714.230.000.0017.10
菜地92.9783.8793.9586.9181.0536.375.283.1534.02
杨树97.4496.4896.7890.2295.0486.6895.3896.5782.87
玉米96.2094.4998.6494.9794.2379.0393.2294.0873.82
梨树97.7996.8299.0196.1894.0685.2486.2686.6482.99
大豆66.3885.2593.4680.5980.7471.5098.3998.1260.38
OA97.8496.7298.5394.7294.7286.4189.9089.7783.76
AA93.5293.2295.5388.8687.4075.3876.0275.9171.89
KC94.2196.1898.2993.8393.8584.1676.3276.2581.07
Table 3  Classification accuracy of Xiong'an data
riLBPSLICEMPDMPAP3D-GaborJBFGF
印第安纳364.20225.3366.3266.66357.48149.3200.210.15
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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