|Place of Conferral||中科院自动化所|
|Keyword||地基可见光云图 地基红外云图 云状识别 云量预测 云顶高融合 分辨率选择|
1. 提出了一种基于稳定局部二值模式（Local Binary Pattern，LBP）的地基可见光云图的云状识别方法
针对传统一致LBP描述子不能捕获地基可见光云纹理图像的全部主要模式，导致低的云状识别性能，充分考虑云图的特点：同一张云图中不同模式的出现频率以及不同云图中相同模式的出现频率变化都非常大，跨越几个数量级，提出了一种基于所有旋转不变LBP模式出现频率排序平均的稳定LBP特征抽取方法。该方法获取了地基云图的最频繁出现的LBP模式，使提出的特征更具分类判别性，同时对噪声和LBP模式出现频率的变化具有更好的鲁棒性。实验结果进一步验证了提出方法的性能统计意义上显著优于其它常用的五种LBP类方法的性能，与卷积神经网络（Convolutional Neural Network，CNN）方法的性能可比，但具有更小的计算开销。
4. 提出了一种基于ARIMA（Autoregressive Integrated Moving Average）时间序列模型的短期云量预测方法
Clouds are the external reflection of atmospheric dynamics, heat, and hydrological cycle. They play an extremely important role on climate and the Earth's energy budget balance. How to accurately and timely acquire the cloud information plays an important role in atmospheric science, atmospheric environment monitoring, climate research, weather forecast, weather modification, and national economy and military fields. The automatic ground-based cloud image observation is a recently developed cloud observation technology, which observes clouds from top to bottom. The obtained data have high quality and temporal-spatial resolution. Ground-based cloud observation can make up the disadvantages of satellite observation, and improve the accuracy and timeliness of cloud observation. Thus, this study may provide technical support for the automated ground-based cloud observation, provide data support for satellite remote sensing data verification and fusion, and improve the comprehensiveness and data quality of cloud observation data. Furthermore, it may develop a new research field for image understanding on objective scenes with natural objects. It has important theoretical significance and application value.
The characteristic of clouds is very complicated. Currently, the cloud observation mainly focuses on the study of cloud type, cloud coverage, and cloud height. Thus, this study performs the cloud type recognition, cloud coverage prediction, and cloud height integration by combining the pattern recognition and machine learning techniques and the characteristics of ground-based cloud images. The main contributions are summarized as follows:
1. A Ground-based Cloud Classification Method by Learning Stable Local Binary Patterns (LBPs)
The conventional uniform LBP approach cannot capture all the dominant patterns in ground-based visible cloud texture images, thereby resulting in low classification performance. By considering the characteristic of cloud images: not only for different patterns in a cloud image, but also for a pattern in different cloud images, the changes of the occurrence frequencies of the patterns are significant in orders of magnitude, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method captures the most frequently occurred LBP patterns of ground-based cloud images, which makes the captured features more discriminative. The proposed descriptor is more robust to noise and the changes of the occurrence frequencies of LBP patterns. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity.
2. A Feature Learning and Cloud Type Recognition Method Based on the Multiple Source Data Fusion of Ground-based Infrared and Visible Light Images
Currently, ground-based cloud image observations generally include two ways of infrared and visible light. These two kinds of cloud image observations attain different useful information of clouds. However, they are only independently analyzed and simply compared in the current study. The useful information is not fully utilized and integrated. The classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP type features is proposed to implement cloud classification by encoding the joint distribution of LBP type patterns in different observations, which captures the correlation between two observations and more discriminative information. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.
3. A Selection Criterion for Resolution of Ground-based Cloud Images Based on the Regularized KL (Kullback-Leibler) Divergence
In ground-based cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when image processing and analysis 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 what degree the original image should be resized and whether useful information is lost in this resizing operation. This study demonstrates that information loss is inevitable. However, this problem has been always neglected in previous literature and the original image is arbitrarily resized without any criterion. Thus, a selection criterion for the optimal resolution of ground-based cloud images based on the regularized KL divergence is constructed, which can provide a helpful guidance for the selection of the optimal resolution. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion.
4. A Short-term Cloud Coverage Prediction Method Using the ARIMA (Autoregressive Integrated Moving Average) Time Series Model
The above-mentioned cloud image analysis is performed based on the single cloud image, however, the cloud-measuring devices on the ground actually take one image of the clouds every few minutes and collect a series of cloud images. The cloud coverages computed from the ground-based cloud images are a time series, and thus the neighbouring cloud coverages over continue time are correlated. However, in traditional prediction techniques, the correlation is always neglected. Thus, an ARIMA time series statistical model is used to predict the short-term cloud coverage, which can effectively capture the correlation between cloud coverages and improve the prediction performance. Experimental results on a collected time series database of cloud coverage show the superiority of the proposed technique.
5. An Integration Technique of Cloud Top Heights Retrieved from Satellite, Radiosonde, and Ground-based Cloud Radar Observations
Cloud top height is typically obtained via three observations, namely, satellite, radiosonde and ground-based radar, with their corresponding strengths and weaknesses. Traditionally, many studies have focused on independent comparison and consistency analysis of CTHs retrieved from different observations. The researches on how to improve the reliability of the CTH observation by integrating multiple cloud measurements are rare in the literature. However, it may be of great significance for practical meteorological forecast and disaster prevention. Thus, in this study, an integration technique of different CTHs retrieved from meteorological satellite, radiosonde and ground-based cloud radar observations is provided by using Bayesian decision theory. Experimental results show that the integration observations improve the accuracy and correlation of single observations.
|王钰. 地基云图观测、分析与识别方法研究[D]. 中科院自动化所. 中科院自动化所,2019.|
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