Monitoring of volcanic ash cloud from heterogeneous data using feature fusion and convolutional neural networks–long short-term memory

Heterogeneous data have become a key issue restricting the monitoring accuracy of volcanic ash cloud and rapid application of remote sensing. In view of the characteristics of classification and heterogeneous data in volcanic ash cloud monitoring, a monitoring method of volcanic ash cloud using feature fusion, convolutional neural networks (CNN) and long short-term memory (LSTM) (FF–CNN–LSTM) is presented in this paper. In this method, firstly, the target features in image are identified by CNN and the time sequence information in text is extracted by LSTM. Secondly, the mapping relationship between text information and image features is learned by fusing image target and text high-level features, and then the identification and diffusion of volcanic ash cloud from heterogeneous data containing only image and text were performed. Finally, the presented FF–CNN–LSTM method is tested by the simulation experiment and the true heterogeneous data of Enta volcanic ash cloud case. The experimental results show that compared with the single CNN, LSTM and the simple combination between CNN and LSTM (CNN–LSTM), the presented FF–CNN–LSTM method in this paper can be fitted with less training steps and has high accuracy and low loss rate; the obtained distribution of volcanic ash cloud is clear and intuitive and has the characteristics with fewer model parameters, simple calculation and high accuracy. It also shows the feasibility and effectiveness of the presented method in volcanic ash cloud monitoring to some extent. The results reveal that the presented method can potentially contribute the monitoring of volcanic ash cloud and disaster prevention and mitigation.