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

Chen Chen; Libing Zhou; Jianzhong Guo; Wei Li; Hongjun Su; Fangda Guo

doi:10.1109/BigMM.2015.23

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

Chen Chen
, Libing Zhou
, Jianzhong Guo
, Wei Li
, Hongjun Su
, Fangda Guo

University of Texas at Dallas
Wuhan Textile University
Beijing University of Chemical Technology
Hohai University
University of Pavia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

44 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	324-329
Number of pages	6
ISBN (Electronic)	9781479986880
DOIs	https://doi.org/10.1109/BigMM.2015.23
Publication status	Published - 9 Jul 2015
Externally published	Yes
Event	1st IEEE International Conference on Multimedia Big Data, BigMM 2015 - Beijing, China Duration: 20 Apr 2015 → 22 Apr 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015

Conference

Conference	1st IEEE International Conference on Multimedia Big Data, BigMM 2015
Country/Territory	China
City	Beijing
Period	20/04/15 → 22/04/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Gabor filtering
extreme learning machine
land-use sence classification
local binary patterns

Access to Document

10.1109/BigMM.2015.23

Cite this

Chen, C., Zhou, L., Guo, J., Li, W., Su, H., & Guo, F. (2015). Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification. In Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015 (pp. 324-329). Article 7153908 (Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2015.23

Chen, Chen ; Zhou, Libing ; Guo, Jianzhong et al. / Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification. Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 324-329 (Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015).

@inproceedings{c070f8a1950b41d2a0082664bd2c601e,

title = "Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification",

abstract = "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.",

keywords = "Gabor filtering, extreme learning machine, land-use sence classification, local binary patterns",

author = "Chen Chen and Libing Zhou and Jianzhong Guo and Wei Li and Hongjun Su and Fangda Guo",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 1st IEEE International Conference on Multimedia Big Data, BigMM 2015 ; Conference date: 20-04-2015 Through 22-04-2015",

year = "2015",

month = jul,

day = "9",

doi = "10.1109/BigMM.2015.23",

language = "English",

series = "Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015",

address = "United States",

}

Chen, C, Zhou, L, Guo, J, Li, W, Su, H & Guo, F 2015, Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification. in Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015., 7153908, Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015, Institute of Electrical and Electronics Engineers Inc., pp. 324-329, 1st IEEE International Conference on Multimedia Big Data, BigMM 2015, Beijing, China, 20/04/15. https://doi.org/10.1109/BigMM.2015.23

Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification. / Chen, Chen; Zhou, Libing; Guo, Jianzhong et al.
Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 324-329 7153908 (Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification

AU - Chen, Chen

AU - Zhou, Libing

AU - Guo, Jianzhong

AU - Li, Wei

AU - Su, Hongjun

AU - Guo, Fangda

PY - 2015/7/9

Y1 - 2015/7/9

N2 - 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.

AB - 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.

KW - Gabor filtering

KW - extreme learning machine

KW - land-use sence classification

KW - local binary patterns

UR - https://www.scopus.com/pages/publications/84941204620

U2 - 10.1109/BigMM.2015.23

DO - 10.1109/BigMM.2015.23

M3 - Conference contribution

AN - SCOPUS:84941204620

T3 - Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015

SP - 324

EP - 329

BT - Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st IEEE International Conference on Multimedia Big Data, BigMM 2015

Y2 - 20 April 2015 through 22 April 2015

ER -

Chen C, Zhou L, Guo J, Li W, Su H, Guo F. Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification. In Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 324-329. 7153908. (Proceedings - 2015 IEEE International Conference on Multimedia Big Data, BigMM 2015). doi: 10.1109/BigMM.2015.23

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

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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