Nonlocal graph theory based transductive learning for hyperspectral image classification

被引:30
|
作者
Huang, Baoxiang [1 ,5 ]
Ge, Linyao [1 ]
Chen, Ge [2 ]
Radenkovic, Milena [3 ]
Wang, Xiaopeng [1 ]
Duan, Jinming [4 ]
Pan, Zhenkuan [1 ]
机构
[1] Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Peoples R China
[2] Ocean Univ China, Coll Informat Sci & Engn, Qingdao 266100, Peoples R China
[3] Univ Nottingham, Sch Comp Sci & Informat Technol, Nottingham NG8 1BB, England
[4] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
[5] Univ Nottingham, Nottingham, England
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Transductive learning; Nonlocal graph; Label propagation; Variational method; Alternating direction method of multipliers; Hyperspectral image classification; SPECTRAL-SPATIAL CLASSIFICATION; DIMENSIONALITY REDUCTION; SPARSE REPRESENTATION; CLASSIFIERS; INFORMATION; FRAMEWORK;
D O I
10.1016/j.patcog.2021.107967
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hyperspectral Image classification plays an important role in the maintenance of remote image analysis, which has been attracting a lot of research interest. Although various approaches, including unsupervised and supervised methods, have been proposed, obtaining a satisfactory classification result is still a challenge. In this paper, an efficient transductive learning method using variational nonlocal graph theory for hyperspectral image classification is proposed. First, the nonlocal vector neighborhood similarity is employed to build sparse graph representation. Then the variational segmentation framework is extended to label space, and the vectorization nonlocal energy function is constructed. Next, a fast comprehensive alternating minimization iteration algorithm is designed to implement labels transductive learning. At the same time, the labeled sample constraints are doubled ensured with simplex projection. Finally, experiments on six widely used hyperspectral image datasets are implemented, compared with other state-of-the-art classification methods, the classification results demonstrate that the proposed method has higher classification performance. Benefiting from graph theory and transductive idea, the proposed classification method can propagate labels and overcome the very high dimensionality and limited labeling problem to some extent. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:18
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