Hyperspectral image classification with imbalanced data based on oversampling and convolutional neural network
文献类型：会议论文
| 作者 | Cai, Lei1,2; Zhang, Geng2
|
| 出版日期 | 2019 |
| 会议日期 | 2019-07-07 |
| 会议地点 | Beijing, China |
| 关键词 | hyperspectral classification imbalanced data SMOTE, oversampling convolutional neural network |
| 卷号 | 11342 |
| DOI | 10.1117/12.2543458 |
| 其他题名 | Cai, Lei(1,2); Zhang, Geng(1) |
| 英文摘要 | Data imbalance is a common problem in hyperspectral image classification. The imbalanced hyperspectral data will seriously affect the final classification performance. To address this problem, this paper proposes a novel solution based on oversampling method and convolutional neural network. The solution is implemented in two steps. Firstly, SMOTE(Synthetic Minority Oversampling Technique) is used to enhance the data of minority classes. In the minority classes, SMOTE method is used to generate new artificial samples, and then the new artificial samples are added to the minority classes, so that all classes in the training dataset can reach to the balanced distribution. Secondly, According to the data characteristics of hyperspectral image, a convolutional neural network is constructed for classifying the hyperspectral image. The balanced training data set is used to train the convolutional neural network. We experimented with the proposed solution on the Indian Pines, Pavia University dataset. The experimental results show that the proposed solution can effectively solve the problem of imbalanced hyperspectral data and improve the classification performance. © 2019 SPIE. |
| 产权排序 | 1 |
| 会议录 | AOPC 2019: AI in Optics and Photonics
 |
| 会议录出版者 | SPIE |
| 语种 | 英语 |
| ISSN号 | 0277786X;1996756X |
| 源URL | [http://ir.opt.ac.cn/handle/181661/93184]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing; 100049, China 2.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China; |
| 推荐引用方式 GB/T 7714 | Cai, Lei,Zhang, Geng. Hyperspectral image classification with imbalanced data based on oversampling and convolutional neural network[C]. 见:. Beijing, China. 2019-07-07. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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