Cloud Shape Classification System Based on Multi-Channel CNN and Improved FDM
Mengyang Zhao, Chorng Hwa Chang, Wenbin Xie, Zhou Xie, Jinyong Hu
Abstract
This paper presents a new meteorological photo classification system based on the Multi-channel Convolutional Neural Network (CNN) and improved Frame Difference Method (FDM). This system can work in an embedded system with limited computational resources and categorize cloud observation photos captured by ground cameras. We propose the improved FDM extractor to detect and extract cloud-like objects from large photos into small images. Then, these small images are sent to a Multi-channel CNN image classifier. We construct the classifier and train it on the photo-set that we established. After combining the extractor and classifier to form the classification system, the images can be classified into three different types of clouds, namely, cumulus, cirrus and stratus, based on their meteorological features. The testing phase uses 200 actual photos of real scenes as the experimental data. The results show that the classification accuracy can reach 94%, which indicates that the system has a competitive classification ability. Moreover, the time cost and computational resource consumption for image recognition are greatly reduced. By using this system, meteorologists can lighten their workload of processing meteorological data.