Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System
文献类型：期刊论文
| 作者 | Kong, Yi2; Wang, Xuesong2; Cheng, Yuhu2; Chen, C. L. Philip1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 10期号:5页码:13 |
| 关键词 | hyperspectral imagery classification broad learning semi-supervised class-probability structure |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs10050685 |
| 通讯作者 | Cheng, Yuhu(yhch@cumt.edu.cn) |
| 英文摘要 | Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is introduced into HSI classification. Firstly, to make full use of abundant spectral and spatial information of hyperspectral imagery, hierarchical guidance filtering is performing on the original HSI to get its spectral-spatial representation. Then, the class-probability structure is incorporated into the broad learning model to obtain a semi-supervised broad learning version, so that limited labeled samples and many unlabeled samples can be utilized simultaneously. Finally, the connecting weights of broad structure can be easily computed through the ridge regression approximation. Experimental results on three popular hyperspectral imagery datasets demonstrate that the proposed method can achieve better performance than deep learning-based methods and conventional classifiers. |
| WOS关键词 | NEURAL-NETWORK ; TEMPERATURE ; ALGORITHM ; GRAPH |
| 资助项目 | National Natural Science Foundation of China[61772532] ; National Natural Science Foundation of China[61472424] ; National Natural Science Foundation of China[61703404] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000435198400027 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27991]  |
| 专题 | 离退休人员 |
| 通讯作者 | Cheng, Yuhu |
| 作者单位 | 1.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 99999, Peoples R China 2.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Jiangsu, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Kong, Yi,Wang, Xuesong,Cheng, Yuhu,et al. Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System[J]. REMOTE SENSING,2018,10(5):13. |
| APA | Kong, Yi,Wang, Xuesong,Cheng, Yuhu,&Chen, C. L. Philip.(2018).Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System.REMOTE SENSING,10(5),13. |
| MLA | Kong, Yi,et al."Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System".REMOTE SENSING 10.5(2018):13. |
入库方式： OAI收割
来源：自动化研究所
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