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Remote Sens. 2016, 8(6), 483; doi:10.3390/rs8060483

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

1
College of Information Science and Technology, Beijing University of Chemical Technology, 100029 Beijing, China
2
Department of Electrical Engineering, University of Texas at Dallas, Dallas, TX 75080, USA
3
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail
Received: 18 February 2016 / Revised: 18 May 2016 / Accepted: 30 May 2016 / Published: 8 June 2016
View Full-Text   |   Download PDF [4703 KB, uploaded 8 June 2016]   |  

Abstract

An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%). View Full-Text
Keywords: remote sensing image scene classification; completed local binary patterns; multi-scale analysis; fisher vector; extreme learning machine remote sensing image scene classification; completed local binary patterns; multi-scale analysis; fisher vector; extreme learning machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, L.; Chen, C.; Li, W.; Du, Q. Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors. Remote Sens. 2016, 8, 483.

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