Land-use scene classification using multi-scale completed local binary patterns. Issue 4 (April 2016)

• Record Type: Journal Article
• Authors: Chen, Chen; Zhang, Baochang; Su, Hongjun; Li, Wei; Wang, Lu
• Is Part Of: Signal, image and video processing. Volume 10:Issue 4(2016)
• Page Start: 745
• Page End: 752
• Publication Date: 2016-04
• Subjects: Land-use scene classification -- Multi-scale analysis -- Local binary patterns -- Extreme learning machine; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0804-2
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract 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.