# Deep Learning-Based Water Crystal Classification
## 2 Related Works  
With a research focus to improve precipitation measurements and forecast for over 50 years, scientific studies of meteorology and weather include the study of snowflakes, ice crystals, and water crystals. Snowflake studies provide some of the most detailed evidence of climate change. It impacts atmospheric science. One of the first attempts to catalog snowflakes was made in the 1930s by [PERSON] who created a method of photographing snowflakes in 1931, using a microscope attached to a camera. The Bentley Snow Crystal Collection ([[https://snowflakebentley.com/](https://snowflakebentley.com/)]([https://snowflakebentley.com/](https://snowflakebentley.com/)) accessed on 15 October 2020) includes about 6125 items. A general classification of snow crystals \(T_{a}-s\) diagram was proposed by [PERSON] [3], which provides the most perfect classification from a physical point of view, with 7 categories. These categories include needles, columns, fern-like crystals developed in one plane, the combination of column and plane crystals, armed crystals, and irregular crystals. The crystal images were collected from a slope of Mount Takachi, near the center of Hokkaido Island. [PERSON] [4] published an improved version of [PERSON]'s classification, with the modification of and a supplement to [PERSON]'s classification of snow crystals. The results were obtained by laboratory experiments and from meteorological observation. The new classification provides temperature and humidity conditions, which can describe the meteorological differences in groups of asymmetric or modified types of snow crystals. It provides 80 categories, modified from [PERSON]'s categories and adding some new categories as well. Thirty thousand microscopic photographs of snow crystals taken by the Cloud Physics Group were used in their research. [PERSON] and his team [5] proposed a new classification with 121 categories to classify snow crystals, ice crystals, and solid precipitation particles. They qualified their classification as \"global scale\" or \"global\" because their observations were performed from the middle latitudes (Japan) to polar regions. This classification consisted of three levels: general, intermediate, and elementary--which are composed of 8, 39, and 121 categories, respectively. Interestingly, this classification can be used not only for snow crystals but also for ice crystals. The deep learning method has been widely applied in many research fields, especially with image datasets. However, it faces the problem of working from a limited dataset. Fortunately, with the advent of image collection methods, a method to collect snowflake images was proposed: the Multi-Angle Snowflake Camera (MASC) [12]. It was developed to address the need for high-resolution multi-angle imaging of hydrometeors in freefall and has resulted in datasets comprising millions of images of falling snowflakes. Several studies have been published resulting from this development. A new method to automatically classify solid hydrometeors based on MASC images was presented by [PERSON] et al. [13]. In this research, they proposed a regularized multinomial logistic regression (MLR) model to output the probabilistic information of MASC images. That probability is then weighed on the three stereoscopic views of the MASC to assign a unique label to each hydrometeor. The MLR model was trained using more than 3000 MASC images labeled by visual inspection. This model achieved very high performance with a 95% accuracy. [PERSON] et al. [6] published an automatic method to classify snowflakes, collected via Multi-Angle Snowflake Camera (MASC). The training dataset contains 1400 MASC images. They used a convolutional neural network and residual network which had been pre-trained with ImageNet as a back-bone for their model. Snowflakes were sorted by geometrics and divided into 6 distinct classes. Then, the degrees of rimming was decided by another training process, which has three distinct classes.