Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples
文献类型:期刊论文
作者 | Wang, Wenning1,2,3; Liu, Xuebin2; Mou, Xuanqin1; Sun, Li3 |
刊名 | CANADIAN JOURNAL OF REMOTE SENSING |
出版日期 | 2018-11-02 |
卷号 | 44期号:6页码:575-587 |
ISSN号 | 0703-8992;1712-7971 |
DOI | 10.1080/07038992.2019.1572500 |
产权排序 | 1 |
英文摘要 | Hyperspectral classification with limited training samples is challenging. The current work lies in two aspects: first, we change the statistical distribution of samples by iterative filtering based on the guide images. The filter is called a Simplified Bilateral Filter (SBF), which is a modified bilateral filter for clustering samples. Secondly, new structural convolution kernels are used to generate new hyperspectral data. Finally, the class label of the test sample after dimension reduction is determined by OMP classification or SVM classification. Experimental results on two hyperspectral datasets demonstrate the effectiveness of the proposed feature extraction method in improving classification accuracy with limited training samples. |
语种 | 英语 |
出版者 | TAYLOR & FRANCIS INC |
WOS记录号 | WOS:000463914400002 |
源URL | [http://ir.opt.ac.cn/handle/181661/31379] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Wang, Wenning |
作者单位 | 1.Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian, Shaanxi, Peoples R China 2.Xian Inst Opt Precis Mech CAS, Key Lab Spectral Imaging Technol, Xian, Shaanxi, Peoples R China 3.Shandong Agr Univ, Sch Informat Sci & Engn, Tai An, Shandong, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Wenning,Liu, Xuebin,Mou, Xuanqin,et al. Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples[J]. CANADIAN JOURNAL OF REMOTE SENSING,2018,44(6):575-587. |
APA | Wang, Wenning,Liu, Xuebin,Mou, Xuanqin,&Sun, Li.(2018).Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples.CANADIAN JOURNAL OF REMOTE SENSING,44(6),575-587. |
MLA | Wang, Wenning,et al."Iterative Filtering and Structural Features for Hyperspectral Image Classification with Limited Samples".CANADIAN JOURNAL OF REMOTE SENSING 44.6(2018):575-587. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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