STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION
文献类型：会议论文
| 作者 | Zisha Zhong ; Bin Fan; Jun Bai; Shiming Xiang; Chunhong Pan
|
| 出版日期 | 2017 |
| 会议日期 | 2017-9-17 |
| 会议地点 | Beijing, CHINA |
| 英文摘要 | In this paper, we propose a novel structured binary feature extraction method for hyperspectral image classification. To pursuit high discriminative ability and low memory cost, we resort to applying the learning to hash technique to the traditional spectral-spatial hyperspectral features. We show how the structured information among different kinds of features and different feature groups can be used to learn discriminative binary features for classification. Experiments on two standard benchmark hyperspectral data sets demonstrate the effectiveness of the proposed method. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20354]  |
| 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 |
| 作者单位 | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Zisha Zhong,Bin Fan,Jun Bai,et al. STRUCTURED BINARY FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION[C]. 见:. Beijing, CHINA. 2017-9-17. |
入库方式： OAI收割
来源：自动化研究所
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