Cloud Image Classification Method Based on Deep Convolutional Neural Network

基于深度卷积神经网络的云分类算法

School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received: 16 October 2019

Abstract

Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.

摘要

与卫星遥感图像相比，地面可见光图像虽然覆盖范围有限，但是分辨率更高、云型特征更明显且获取成本大大降低，有利于对局部地区进行持续性气象观测。首次针对地面可见光图像，提出了一种基于深度学习技术的云型图像分类方法。由于数据量有限，传统分类器如支持向量机等无法有效提取不同云的独有特征，而直接训练深度卷积神经网络会导致过拟合。为防止网络过拟合，提出利用迁移学习方法，对预训练模型进行微调。在对6类云型图像进行分类的实验中，本文所提出的网络在测试集上可以获得高达85.19%的正确率。所提出的网络可以直接对数码相机照片进行分类，大大降低了系统成本。