SPARSE REPRESENTATION BASED HYPERSPECTRAL IMAGERY CLASSIFICATION VIA EXPANDED DICTIONARY

被引：0
|
作者：
He, Lin [1 ]
Ruan, Weitong [1 ]
Li, Yuanqing [1 ]
机构：
[1] S China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China
来源：
2012 4TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING (WHISPERS) | 2012年
关键词：
Hyperspectral imagery; classification; sparse representation; dyadic wavelet transform;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Recently, pattern classification and recognition based on sparse representation have seen a surge of interest in many applications. In this article, we present a method of sparse representation based hyperspectral imagery classification via expanded dictionary. The original spectral signatures in hyperspectral imagery are transformed with 1 - D dyadic wavelet transform. Then these wavelet features are combined with the original spectral signatures to form an expanded dictionary. Finally, linear programming is employed to calculate the sparse solution on such a dictionary which was further substituted into related decision rule. Results of experiment on real hyperspectral imagery validate the effectiveness of our method.
引用
收藏
页数：4
相关论文
共 50 条
  • [21] 2D Gabor-based Sparse Representation Classification for Hyperspectral Imagery
    Wu, Kuilin
    Jia, Sen
    2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2018, : 713 - 720
  • [22] Bayesian Classification of Hyperspectral Imagery Based on Probabilistic Sparse Representation and Markov Random Field
    Xu, Linlin
    Li, Jonathan
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (04) : 823 - 827
  • [23] HYPERSPECTRAL IMAGE CLASSIFICATION VIA KERNEL SPARSE REPRESENTATION
    Chen, Yi
    Nasrabadi, Nasser M.
    Tran, Trac D.
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011, : 1233 - 1236
  • [24] Hyperspectral Image Classification via Kernel Sparse Representation
    Chen, Yi
    Nasrabadi, Nasser M.
    Tran, Trac D.
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (01): : 217 - 231
  • [25] HYPERSPECTRAL IMAGE CLASSIFICATION VIA JOINT SPARSE REPRESENTATION
    Hsu, Pai-Hui
    Cheng, Ying-Ying
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 2997 - 3000
  • [26] A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery
    Zhang, Hongyan
    Li, Jiayi
    Huang, Yuancheng
    Zhang, Liangpei
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (06) : 2056 - 2065
  • [27] Weighted Joint Sparse Representation Hyperspectral Image Classification Based on Spatial-Spectral Dictionary
    Chen Shanxue
    He Yufeng
    ACTA OPTICA SINICA, 2023, 43 (01)
  • [28] Dictionary learning based sparse representation for hyperspectral anomaly detection
    Tang, Yidong
    Huang, Shucai
    Ling, Qiang
    Zhong, Yu
    Qiangjiguang Yu Lizishu/High Power Laser and Particle Beams, 2015, 27 (11):
  • [29] Integration of Spatial and Spectral Information by Means of Sparse Representation-Based Classification for Hyperspectral Imagery
    Jia, Sen
    Xie, Yao
    Zhu, Zexuan
    PROCEEDINGS OF THE 18TH ASIA PACIFIC SYMPOSIUM ON INTELLIGENT AND EVOLUTIONARY SYSTEMS, VOL 2, 2015, : 117 - 126
  • [30] Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification
    Yu, Haoyang
    Gao, Lianru
    Liao, Wenzhi
    Zhang, Bing
    SENSORS, 2018, 18 (06)
12345