SEMI-SUPERVISED CONDITIONAL RANDOM FIELD FOR HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION

被引：4
|
作者：
Wu, Junfeng [1 ]
Jiang, Zhiguo
Zhang, Haopeng
Cai, Bowen
Wei, Quanmao
机构：
[1] Beihang Univ, Image Proc Ctr, Sch Astronaut, Beijing 100191, Peoples R China
来源：
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年
基金：
中国国家自然科学基金;
关键词：
CRF; semi-supervised; hyperspectral; remote sensing; classification; CONSTRAINT;
D O I：
10.1109/IGARSS.2016.7729675
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：
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM), after extended into the composite kernel type, is defined as the association potential. And the Potts model is utilized as the interaction potential. The ssCRF is evaluated on the two benchmarks and the results show the effectiveness of ssCRF.
引用
收藏
页码：2614 / 2617
页数：4
相关论文
共 50 条
  • [31] Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest
    Lu, Xiaochen
    Zhang, Junping
    Li, Tong
    Zhang, Ye
    REMOTE SENSING, 2017, 9 (09)
  • [32] SEMI-SUPERVISED LEARNING BY DOMAIN ADAPTATION FOR HYPERSPECTRAL IMAGE CLASSIFICATION
    Deshpande, Shailesh S.
    Banolia, Chaman
    Balamuralidhar, P.
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 6009 - 6012
  • [33] Semi-supervised graph-based hyperspectral image classification
    Camps-Valls, Gustavo
    Bandos, Tatyana V.
    Zhou, Dengyong
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2007, 45 (10): : 3044 - 3054
  • [34] ITERATIVE SEMI-SUPERVISED MANIFOLD ALIGNMENT FOR HYPERSPECTRAL IMAGE CLASSIFICATION
    Zhu, Linghui
    Ma, Li
    Li, Xingmei
    2022 12TH WORKSHOP ON HYPERSPECTRAL IMAGING AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2022,
  • [35] Semi-supervised dual path network for hyperspectral image classification
    Huang H.
    Zhang Z.
    Ji L.
    Li Z.
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2022, 30 (15): : 1889 - 1904
  • [36] SEMI-SUPERVISED ACTIVE LEARNING FOR URBAN HYPERSPECTRAL IMAGE CLASSIFICATION
    Dopido, Inmaculada
    Li, Jun
    Plaza, Antonio
    Bioucas-Dias, Jose M.
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 1586 - 1589
  • [37] Unified active and semi-supervised learning for hyperspectral image classification
    Zengmao Wang
    Bo Du
    GeoInformatica, 2023, 27 : 23 - 38
  • [38] Semi-supervised bundle manifold learning for hyperspectral image classification
    Li, Zhi-Min
    Zhang, Jie
    Huang, Hong
    Jiang, Tao
    Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2015, 23 (05): : 1434 - 1442
  • [39] A novel semi-supervised learning framework for hyperspectral image classification
    Ye, Zhijing
    Li, Hong
    Song, Yalong
    Wang, Jianzhong
    Benediktsson, Jon Atli
    INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, 2016, 14 (02)
  • [40] Hierarchical clustering and stochastic distance for indirect semi-supervised remote sensing image classification
    Sapucci, Gabriela Ribeiro
    Negri, Rogerio Galante
    SN APPLIED SCIENCES, 2019, 1 (03):
12345