Identification of Cloud Types for Meteorological Satellite Images: A Character-Based CNN-LSTM Hybrid Caption Model

Satellite Clouds have a significant role in the weather system and climate change, and the distribution of clouds is always strongly tied to a particular meteorological phenomenon. In this paper, an automatic identification of cloud types is proposed using a hybrid approach of convolution neural network (CNN) and bidirectional character based long short-term memory (LSTM). The large-scale cloud image database for meteorological research (LSCIDMR) of the ground truth images related to weather types is used as the input for the proposed work. Three types of CNN models, such as inception v3 network, Vgg-16 and Alexnet, are used separately and subsequently, the results are compared, in terms of precision, recall, and F1 score, to obtain the best among them. The LSTM is trained with our self-trained dictionary having tokens. The image features and single character are merged into a single step. It produces the output as the next character to come and so on.