期刊
SIGNAL IMAGE AND VIDEO PROCESSING
卷 10, 期 4, 页码 745-752出版社
SPRINGER LONDON LTD
DOI: 10.1007/s11760-015-0804-2
关键词
Land-use scene classification; Multi-scale analysis; Local binary patterns; Extreme learning machine
In this paper, we introduce the completed local binary patterns (CLBP) operator for the first time on remote sensing land-use scene classification. To further improve the representation power of CLBP, we propose a multi-scale CLBP (MS-CLBP) descriptor to characterize the dominant texture features in multiple resolutions. Two different kinds of implementations of MS-CLBP equipped with the kernel-based extreme learning machine are investigated and compared in terms of classification accuracy and computational complexity. The proposed approach is extensively tested on the 21-class land-use dataset and the 19-class satellite scene dataset showing a consistent increase on performance when compared to the state of the arts.
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