Energy-based cloud detection in multispectral images based on the SVM technique
文献类型：期刊论文
| 作者 | Y.L.Sui; B.He; T.J.Fu |
| 刊名 | International Journal of Remote Sensing
 |
| 出版日期 | 2019 |
| 卷号 | 40期号:14页码:5530-5543 |
| 关键词 | landsat,Remote Sensing,Imaging Science & Photographic Technology |
| ISSN号 | 0143-1161 |
| DOI | 10.1080/01431161.2019.1580788 |
| 英文摘要 | In this paper, the energy characteristics of Gabor texture are used for cloud detection in high-resolution multispectral images. First, the satellite remote-sensing image is divided into superpixels using simple linear iterative clustering (SLIC), and then, the energy characteristics of Gabor texture and spectral characteristics are computed by extracting the texture features of the superpixels. The features of the cloud superpixels are used as the learning sample of the support vector machine (SVM) classifier, and a classification model is obtained by training the SVM classifier. Finally, a cloud-detection experiment is conducted for various sensor images with three visible bands and one near-infrared band. The experimental results showed that the proposed method provides an excellent average overall accuracy for thick and thin clouds in a complex background of forests, harbours, snow and mountains. The characteristic parameters of this paper are not limited by the image parameters; thus, they provide good results and universality for various types of sensors. |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/63065]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | Y.L.Sui,B.He,T.J.Fu. Energy-based cloud detection in multispectral images based on the SVM technique[J]. International Journal of Remote Sensing,2019,40(14):5530-5543. |
| APA | Y.L.Sui,B.He,&T.J.Fu.(2019).Energy-based cloud detection in multispectral images based on the SVM technique.International Journal of Remote Sensing,40(14),5530-5543. |
| MLA | Y.L.Sui,et al."Energy-based cloud detection in multispectral images based on the SVM technique".International Journal of Remote Sensing 40.14(2019):5530-5543. |
入库方式： OAI收割
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