Apply CNN-XGBoost into Weather Image Recognition

Abstract

This study implements some hybrid deep learning network models for weather image classification. This study proposes to apply a hybrid model, namely CNN-XGBoost model to test its performance, in comparison with other simple Convolutional Neural Network (CNN) model with softmax and in addition with the other hybrid models, namely CNN-SVC, CNN-Decision Tree, CNN-AdaBoost, Multi-layer Perceptron Classifier which are all applied into the same problem of weather image classification. The models apply an identical test dataset which is a set of 11 different image classes that are collected from different resources of weather images with various kinds of weather phenomena. The test results show that the CNN-XGBoost gives the best results, which is suitable for application in evaluating weather images. The aim of this study is to check whether what kind of hybrid deep learning has the best performance in the problem of weather image classification, not focus on accuracy improvement of the deep learning models in classification problem.