SVM or deep learning? A comparative study on remote sensing image classification
文献类型：期刊论文
| 作者 | Liu, Peng; Choo, Kim-Kwang Raymond; Wang, Lizhe; Huang, Fang |
| 出版日期 | 2016 |
| 卷号 | 0期号:0页码:1-13 |
| 英文摘要 | With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods. © 2016 Springer-Verlag Berlin Heidelberg |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162902606843 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39596]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 推荐引用方式 GB/T 7714 | Liu, Peng,Choo, Kim-Kwang Raymond,Wang, Lizhe,et al. SVM or deep learning? A comparative study on remote sensing image classification[J],2016,0(0):1-13. |
| APA | Liu, Peng,Choo, Kim-Kwang Raymond,Wang, Lizhe,&Huang, Fang.(2016).SVM or deep learning? A comparative study on remote sensing image classification.,0(0),1-13. |
| MLA | Liu, Peng,et al."SVM or deep learning? A comparative study on remote sensing image classification".0.0(2016):1-13. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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