A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification | |
Wang Y(王钰)![]() ![]() ![]() ![]() | |
Source Publication | IEEE Transactions on Geoscience and Remote Sensing
![]() |
2019 | |
Issue | 3Pages:1358-1367 |
Abstract | In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback–Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features. |
Keyword | Cloud Image Classification Local Binary Patterns Resolution Selection Kullback– Leibler (Kl) Divergence |
Indexed By | SCI |
WOS ID | WOS:000460321300011 |
Citation statistics | |
Document Type | 期刊论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/23629 |
Collection | 复杂系统管理与控制国家重点实验室_影像分析与机器视觉 |
Affiliation | 中国科学院自动化研究所 |
First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
Recommended Citation GB/T 7714 | Wang Y,Wang CH,Shi CZ,et al. A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification[J]. IEEE Transactions on Geoscience and Remote Sensing,2019(3):1358-1367. |
APA | Wang Y,Wang CH,Shi CZ,&Xiao BH.(2019).A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification.IEEE Transactions on Geoscience and Remote Sensing(3),1358-1367. |
MLA | Wang Y,et al."A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification".IEEE Transactions on Geoscience and Remote Sensing .3(2019):1358-1367. |
Files in This Item: | Download All | |||||
File Name/Size | DocType | Version | Access | License | ||
A Selection Criterio(4352KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment