中圖分類號(hào)：TP412.15
文獻(xiàn)標(biāo)識(shí)碼：A
DOI： 10.19358/j.issn.2096-5133.2020.03.011
引用格式：王敏，周樹道，劉展華，等.卷積神經(jīng)網(wǎng)絡(luò)識(shí)別地基云圖的數(shù)據(jù)庫(kù)建立及處理方法［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（3）：56-61.

Establishment and processing of Groundbased cloud image database for CNN

Abstract： Convolutional neural network (CNN) has an extraordinary ability to learn features from samples,and training requires a large number of image samples with labels.Therefore,it is the first and very important step to establish cloud image sample bank when using convolutional neural network to study the groundbased cloud image.Firstly,three cloud image sample libraries are acquired by means of digital camera direct shooting,downloading from the Internet,acquiring from publicly released cloud image books,and shooting by allsky camera.Then,the resolution,noise and number of images in the three sample libraries are analyzed.Then,bilinear interpolation and data enhancement are used to normalize the sample database.Finally,CNN,LBP,Heinle feature and Textonbased method are used to verify the cloud recognition of the enhanced data set.The experimental results show that the improved data can effectively solve the problems of convolution neural network for small sample data recognition such as low rate and incomplete network operation, and lays a foundation for the application of convolutional neural network in the recognition of groundbased cloud image.

Key words : convolutional neural network;supervised learning;sample bank;normalization