A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification
Wang Y(王钰); Wang CH(王春恒); Shi CZ(史存召); Xiao BH(肖柏华)
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
2019
Issue3Pages: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.

KeywordCloud Image Classification Local Binary Patterns Resolution Selection Kullback– Leibler (Kl) Divergence
Indexed BySCI
WOS IDWOS:000460321300011
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Document Type期刊论文
Identifierhttp://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 AffilicationInstitute 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.
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