Research On Classification Method Of Ground-based Cloud Images Based On Deep Learning

The shape,distribution,and changes of clouds are important indicators of weather changes and affect global climate changes.Therefore,cloud observation has very important research value,and it plays a very important role in aerial target recognition,traffic control and weather forecasting.With the continuous development of ground observation equipment and imaging technology,the observation of ground-based cloud images has changed from manual visual observation to equipment imaging and storage,but the judgment of ground-based cloud images still requires meteorological staff,which is highly subjective and requires huge costs.In this paper,we conducted research on the automatic classification way of ground cloud images,and conducted the following research.This paper proposes a ground-based cloud figure identification arithmetic ground on SVM,which is mostly uncoupled into three roles:pre-treatment,feature extraction and image category.Preprocess the image of clouds on the ground to take out the affection of stew and other elements on the image.Combined with the visual characteristics of the ground-based cloud image,the texture feature of the cloud image is abstracted founded on the gray level cooccurrence matrix,the form feature of the cloud image is abstracted founded on the LBP,and the color characteristic of the cloud figure is segregated founded on the multi-color room.Utilize the S VM organizer to categorize the land-based cloud figure.In order to further improve the precision of classification,this paper raises a ground-based cloud image recognition method founded on deep learning.Set up a comparison experiment to select the basic network model,study the influence of different network models and network depths on the recognition of ground-based cloud images,and select the GoogLeNet network model as the basic network.However,the GoogLeNet network model has many parameters and even over-fitting.In this paper,the ground-based cloud image classification and recognition model enhances network structure in the light of the underlying network GoogLeNet,adds a weakly supervised attention learning mechanism,and improves the accuracy and generalization ability of model recognition,and improves the pooling method.Through comparative experiments,the effectiveness of the improved method in the essay is verified.Aiming at the actual needs of ground-based cloud image identification,a ground-based cloud image identification tool is designed and implemented.The front-end interface is developed and designed using PyQt5 and QT Designer,and the back-end processing is implemented using the Python programming language.Experiments show that the software can effectively identify ground-based cloud images.