Classification of Weather Phenomenon with a New Deep Learning Method Based on Transfer Learning

Abstract

Recognition of weather conditions, which have an important effect on the planning of our daily lives, affects many events from transport to agriculture. Even on an ordinary day, the weather affects many events, from taking children to the market to taking a walk. In addition, in many commercial areas such as agriculture and animal husbandry, many issues from planting and planting time to production are directly or indirectly related to weather conditions. For these reasons, automatic analyses and classification of aerial images will provide significant convenience. New technologies based on deep learning are needed to minimize the errors of experts working in the towers established to monitor weather conditions. Deep learning based systems are preferred because they bring a new perspective to feature extraction and classification approaches in classical machine learning technologies. With deep learning based systems, it is possible to classify by obtaining distinctive features from different weather conditions. In this paper, a pre-trained architecture-based deep learning model is proposed to classify a dataset containing 6877 images of 11 weather conditions. In order to measure the effect of the proposed model on the performance, a comparison with the basic model is performed. The weather classification accuracy of the proposed model in the test set is 88%. This performance result shows that the model is competitive with its competitors. At this point, eleven different weather images can be automatically classified. As a result of the mentioned procedures, this study can be a reference for future weather classification studies.