Survival study on cyclone prediction methods with remote sensing images

Image classification has large interest for many decades in the remote sensing communities to reduce injure caused by cyclones. A cyclone is the leading rotating storm that includes the strong wind and rain. It included the number of interrelated features like eye, cyclone pathway, wind speed, generated storm surges, rainfall intensity and so on. Among the features, it is essential one to find in which direction cyclone travels and it influence the areas increasing the damage to life and assets. The cyclone prediction is a key issue where image intensity described the pattern characteristics at various stages. Many existing works have been designed in cyclone prediction for attaining better prediction accuracy. But, it is difficult to enhance the cyclone prediction accuracy with minimum time complexity. In order to address these issues, cyclone prediction can be carried out using deep leaning methods.