Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study
文献类型：期刊论文
| 作者 | Zhong, Zisha ; Fan, Bin; Ding, Kun; Li, Haichang; Xiang, Shiming; Pan, Chunhong; zszhong@nlpr.ia.ac.cn
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
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| 出版日期 | 2016-08-01 |
| 卷号 | 54期号:8页码:4461-4478 |
| 关键词 | Binary Codes Classification Feature Fusion Hashing Hyperspectral Images |
| DOI | 10.1109/TGRS.2016.2542342 |
| 文献子类 | Article |
| 英文摘要 | Due to the complementary properties of different features, multiple feature fusion has a large potential for hyperspectral imagery classification. At the meantime, hashing is promising in representing a high-dimensional float-type feature with extremely low bit binary codes while maintaining the performance. In this paper, we study the possibility of using hashing to fuse multiple features for hyperspectral imagery classification. For this purpose, we propose a multiple feature fusion framework to evaluate the performance of using different hashing methods. For comparison and completeness, we also have an extensive comparison to five subspace-based dimension reduction methods and six fusion-based methods which are popular solutions to deal with multiple features in hyperspectral image classification. Experimental results on four benchmark hyperspectral data sets demonstrate that using hashing to fuse multiple features can achieve comparable or better performance with the traditional subspace-based dimension reduction methods and fusion-based methods. Moreover, the binary features obtained by using hashing need much less storage and are faster to compute distances with the help of machine instructions. |
| WOS关键词 | GRAY-LEVEL COOCCURRENCE ; FEATURE-SELECTION ; FEATURE-EXTRACTION ; DECISION TREES ; URBAN AREAS ; LIDAR DATA ; PROFILES ; REGRESSION ; KERNELS ; MATRIX |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000381434600008 |
| 资助机构 | National Natural Science Foundation of China(61573352 ; Beijing Natural Science Foundation(4142057) ; 61472119 ; 91338202 ; 91438105) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12629]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 通讯作者 | zszhong@nlpr.ia.ac.cn |
| 作者单位 | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhong, Zisha,Fan, Bin,Ding, Kun,et al. Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(8):4461-4478. |
| APA | Zhong, Zisha.,Fan, Bin.,Ding, Kun.,Li, Haichang.,Xiang, Shiming.,...&zszhong@nlpr.ia.ac.cn.(2016).Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,54(8),4461-4478. |
| MLA | Zhong, Zisha,et al."Efficient Multiple Feature Fusion With Hashing for Hyperspectral Imagery Classification: A Comparative Study".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.8(2016):4461-4478. |
入库方式： OAI收割
来源：自动化研究所
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