Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network
文献类型：期刊论文
| 作者 | Chen, Yushi1; Zhu, Lin1; Ghamisi, Pedram2,3; Jia, Xiuping4; Li, Guoyu5; Tang, Liang6 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2017-12-01 |
| 卷号 | 14期号:12页码:2355-2359 |
| 关键词 | Convolutional neural network (CNN) deep learning feature extraction (FE) Gabor filtering hyperspectral images (HSIs) |
| ISSN号 | 1545-598X |
| DOI | 10.1109/LGRS.2017.2764915 |
| 通讯作者 | Chen, Yushi(chenyushi@hit.edu.cn) ; Tang, Liang(hit_tl@163.com) |
| 英文摘要 | Recently, the capability of deep learning-based approaches, especially deep convolutional neural networks (CNNs), has been investigated for hyperspectral remote sensing feature extraction (FE) and classification. Due to the large number of learnable parameters in convolutional filters, lots of training samples are needed in deep CNNs to avoid the overfitting problem. On the other hand, Gabor filtering can effectively extract spatial information including edges and textures, which may reduce the FE burden of the CNNs. In this letter, in order to make the most of deep CNN and Gabor filtering, a new strategy, which combines Gabor filters with convolutional filters, is proposed for hyperspectral image classification to mitigate the problem of overfitting. The obtained results reveal that the proposed model provides competitive results in terms of classification accuracy, especially when only a limited number of training samples are available. |
| 收录类别 | SCI |
| WOS关键词 | SPATIAL CLASSIFICATION ; PROFILES |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000418116500037 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2558031 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Chen, Yushi; Tang, Liang |
| 作者单位 | 1.Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China 2.German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, D-82234 Wessling, Germany 3.Tech Univ Munich, Signal Proc Earth Observat, D-80333 Munich, Germany 4.Univ New South Wales, Sch Engn & Informat Technol, Canberra, NSW 2600, Australia 5.Chinese Acad Sci, State Key Lab Frozen Soil Engn, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China 6.Harbin Inst Technol, Sch Civil Engn, Harbin 150001, Heilongjiang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Yushi,Zhu, Lin,Ghamisi, Pedram,et al. Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(12):2355-2359. |
| APA | Chen, Yushi,Zhu, Lin,Ghamisi, Pedram,Jia, Xiuping,Li, Guoyu,&Tang, Liang.(2017).Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(12),2355-2359. |
| MLA | Chen, Yushi,et al."Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.12(2017):2355-2359. |
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来源：寒区旱区环境与工程研究所
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