Automatic Classification of Snow Particles

Abstract: The simplest form of a snow particle is a hexagonal prism which can grow into a stellar crystal by growing branches from the six corners of the prism. The snow particle is affected by the temperature and supersaturation in the air, giving its unique form. Manual classification of snow particles based on shape is tedious work. Convolutional Neural Network (CNN) can therefor be of great assistance and are common in automatic image processing. From a data set consisting of 3165 images sorted into 15 shape classes, a sub set of 2193 images and 7 classes was used. The selected classes had the highest number of snow particle images and were used to train, validate and test on. Four data sets were constructed and eight models were used to classify the snow particles into seven classes. To reduce the amount of training data needed pretrained versions of neural networks AlexNet and ResNet50 were used with a technique called transfer learning. The 2193 images make up the first data set, Data set 1. To handle unbalanced classes in the first data set Synthetic Minority Oversampling Technique (SMOTE) was used to increase the number of snow particles in classes with few examples, creating Data set 2. A third data set was constructed to mimic a real world application. The data for training and validation was increased with SMOTE, while the test data only consisted of real snow particles. The performance of both ResNet50 and AlexNet on the data met the requirements for a practical application. However, ResNet50 had a higher overall accuracy, 72%, compared to AlexNet 69% on the evaluated data set. A t-test was conducted with a significance of p < 1·10−8. To enhance the shape of the snow particles a Euclidean Distance Transform (EDT) was used, creating Data set 4. However, this did not increase the accuracy of the trained model. To increase the accuracy of the models more training data of snow particles is needed, especially for classes with few examples. A larger data set would also allow more classes to be included in the classification.