Link the remote sensing big data to the image features via wavelet transformation
文献类型：期刊论文
| 作者 | Wang, Lizhe1; Song, Weijing1; Liu, Peng1 |
| 刊名 | Cluster Computing
 |
| 出版日期 | 2016 |
| 卷号 | 19期号:2页码:793-810 |
| 关键词 | MULTIPLE-SCATTERING DIFFUSE-RADIATION ATMOSPHERES |
| 通讯作者 | Song, Weijing (songweijing_haiou@163.com) |
| 英文摘要 | With the development of remote sensing technologies, especially the improvement of spatial, time and spectrum resolution, the volume of remote sensing data is bigger. Meanwhile, the remote sensing textures of the same ground object present different features in various temporal and spatial scales. Therefore, it is difficult to describe overall features of remote sensing big data with different time and spatial resolution. To represent big data features conveniently and intuitively compared with classical methods, we propose some texture descriptors from different sides based on wavelet transforms. These descriptors include a statistical descriptor based on statistical mean, variance, skewness, and kurtosis; a directional descriptor based on a gradient histogram; a periodical descriptor based on auto-correlation; and a low-frequency statistical descriptor based on the Gaussian mixture model. We analyze three different types of remote sensing textures and contrast the results similarities and differences in three different analysis domains to demonstrate the validity of the texture descriptors. Moreover, we select three factors representing texture distributions in the wavelet transform domain to verify that the texture descriptors could be better to classify texture types. Consequently, the texture descriptors appropriate for describe remote sensing big data overall features with simple calculation and intuitive meaning. © 2016, Springer Science+Business Media New York. |
| 学科主题 | Computer Science |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Theory & Methods |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20161902362104 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39462]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. School of Computer Science, China University of Geoscience, No. 388 Lumo Road, Wuhan 2.430074, China 3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 4.100094, China |
| 推荐引用方式 GB/T 7714 | Wang, Lizhe,Song, Weijing,Liu, Peng. Link the remote sensing big data to the image features via wavelet transformation[J]. Cluster Computing,2016,19(2):793-810. |
| APA | Wang, Lizhe,Song, Weijing,&Liu, Peng.(2016).Link the remote sensing big data to the image features via wavelet transformation.Cluster Computing,19(2),793-810. |
| MLA | Wang, Lizhe,et al."Link the remote sensing big data to the image features via wavelet transformation".Cluster Computing 19.2(2016):793-810. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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