Hyperspectral image classification via discriminative sparse representation with extended LBP texture

Yue Mei Ren, Yan Ning Zhang, Wei Wei

科研成果: 书/报告/会议事项章节会议稿件同行评审

4 引用 (Scopus)

摘要

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.

源语言英语
主期刊名Materials Science, Computer and Information Technology
出版商Trans Tech Publications Ltd
3885-3888
页数4
ISBN(印刷版)9783038351733
DOI
出版状态已出版 - 2014
活动4th International Conference on Materials Science and Information Technology, MSIT 2014 - Tianjin, 中国
期限: 14 6月 201415 6月 2014

出版系列

姓名Advanced Materials Research
989-994
ISSN(印刷版)1022-6680
ISSN(电子版)1662-8985

会议

会议4th International Conference on Materials Science and Information Technology, MSIT 2014
国家/地区中国
Tianjin
时期14/06/1415/06/14

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