Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher VectorsReportar como inadecuado




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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

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





Autor: Longhui Huang 1, Chen Chen 2, Wei Li 1,* and Qian Du 3

Fuente: http://mdpi.com/



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