Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification

被引:32
作者
Chen, Chen [1 ]
Zhou, Libing [2 ]
Guo, Jianzhong [1 ,2 ]
Li, Wei [3 ]
Su, Hongjun [4 ]
Guo, Fangda [5 ]
机构
[1] Univ Texas Dallas, Dept Elect Engn, Richardson, TX 75083 USA
[2] Wuhan Text Univ, Sch Elect & Elect Engn, Wuhan, Peoples R China
[3] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China
[4] Hohai Univ, Sch Earth Sci & Engn, Nanjing, Jiangsu, Peoples R China
[5] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy
来源
2015 1ST IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM) | 2015年
关键词
Gabor filtering; local binary patterns; land-use sence classification; extreme learning machine; EXTREME LEARNING-MACHINE;
D O I
10.1109/BigMM.2015.23
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Remote sensing land-use scene classification has a wide range of applications including forestry, urban-growth analysis, and weather forecasting. This paper presents an effective image representation method, Gabor-filtering-based completed local binary patterns (GCLBP), for land-use scene classification. It employs the multi-orientation Gabor filters to capture the global texture information from an input image. Then, a local operator called completed local binary patterns (CLBP) is utilized to extract the local texture features, such as edges and corners, from the Gabor feature images and the input image. The resulting CLBP histogram features are concatenated to represent an input image. Experimental results on two datasets demonstrate that the proposed method is superior to several existing methods for land-use scene classification.
引用
收藏
页码:324 / 329
页数:6
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