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 dailylives, affects many events from transport to agriculture. Even on an ordinary day, the weather affects manyevents, from taking children to the market to taking a walk. In addition, in many commercial areas such asagriculture and animal husbandry, many issues from planting and planting time to production are directlyor indirectly related to weather conditions. For these reasons, automatic analyses and classification of aerialimages will provide significant convenience. New technologies based on deep learning are needed tominimize the errors of experts working in the towers established to monitor weather conditions. Deeplearning based systems are preferred because they bring a new perspective to feature extraction andclassification approaches in classical machine learning technologies. With deep learning based systems, itis possible to classify by obtaining distinctive features from different weather conditions. In this paper, apre-trained architecture-based deep learning model is proposed to classify a dataset containing 6877 imagesof 11 weather conditions. In order to measure the effect of the proposed model on the performance, acomparison with the basic model is performed. The weather classification accuracy of the proposed modelin the test set is 88%. This performance result shows that the model is competitive with its competitors. Atthis point, eleven different weather images can be automatically classified. As a result of the mentionedprocedures, this study can be a reference for future weather classification studies.