基于压缩感知的地基红外云图云状识别  

Classification of Whole Sky Infrared Cloud Image Using Compressive Sensing

作　　者：韩文宇[1] 刘磊[1] 高太长[1] 李云[1] 胡帅[1] 张孝忠[2] 

机构地区：[1]解放军理工大学气象海洋学院 [2]中国人民解放军65631部队

出　　处：《应用气象学报》2015年第2期231-239,共9页Journal of Applied Meteorological Science

基　　金：国家自然科学基金项目(41205125);公益性行业(气象)科研专项(201206068)

摘　　要：为了对地基全天空红外测云仪获得的云图进行分类,该文从压缩感知理论出发,提出了一种利用云图灰度稀疏性进行云状识别的新方法。首先运用典型云图样本构造冗余字典,然后通过梯度投影(GPSR)算法和正交匹配(OMP)算法求取测试样本在冗余字典中的l^1范式最优解,最后利用残差法和稀疏比例法对云状进行判别并输出。采用压缩感知理论进行云状识别,降低了对特征提取技术的要求,为云状的自动识别提供了新思路,对典型波状云、层状云、积状云、卷云和晴空的总体识别率分别达到75%,91%,70%,85%和93%,平均识别率为82.8%。Cloud type, as an important macroeconomic parameter in cloud detection, plays a mean role in weather forecasting, field meteorological service, aerospace and climate researches. Automatic identification of cloud types is not efficiently resolved. Cloud shapes, texture, color, contour, range, process of change and some other features are used for manual cloud classification, but it is hard to find a nice way to extract effective features for automatic identification. Particularly, infrared images provide less resolution and less color information. A new method is proposed to classify cloud images obtained from the whole sky infrared cloud measuring system （WSIRCMS） from compressive sensing （CS）. Firstly, a redundant dictionary is constructed with typical cloud samples. In order to reduce the computational complexity and computing time, principal component analysis （PCA） and down-sampling is applied to dimension reduction in building up redundant dictionary. It ~s found that classification results tend to be stable and suitable when the feature contribution rate is more than 95~ in PCA or at 16-time down-sampling. Secondly, the optimal solution of paradigm is solved using gradient projection for sparse reconstruction （GPSR） and orthogonal matching pursuit （OMP） algorithms. Sparse algorithm has a certain influence on classification results. There are some negative sparse solutions in GPSR and OMP algorithms, and through the analysis, when the proportion of negative sparse solution is more than 46 %, the classification of residual method is prone to error. Sparse solution may be wrong if the incoherence of different type cannot be guaranteed in establishing redundant dictionary, and the dimension reduction may especially increase the correlation. If the cloud texture, structure feature can be kept in process of dimension reduction and one-dimensional treatment and establishing redundant dictionary is complete, it probably makes better sparse solution. Finally, the residual method and sparse propor

关 键 词：红外云图 压缩感知 稀疏表示 云状识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]