A Neural Networks Approach for Volcanic Ash Detection in the 2019 Raikoke Eruption Using S3-SLSTR Data

• Published in: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
• Main Authors: Petracca I., De Santis D., Corradini S., Guerrieri L., Picchiani M., Merucci L., Stelitano D., Del Frate F., Prata F., Salvucci G., Schiavon G.
• Other Authors: Petracca, I, De Santis, D, Corradini, S, Guerrieri, L, Picchiani, M, Merucci, L, Stelitano, D, Del Frate, F, Prata, F, Salvucci, G, Schiavon, G
• Format: Conference Object
• Language: English
• Published: IEEE 2022
• Subjects: Ash Detection; Neural Networks; SLSTR; MODIS; Settore ING-INF/02; Settore IINF-02/A - Campi elettromagnetici; Eyjafjallajokull; Iceland
• Online Access: https://hdl.handle.net/2108/389851; https://doi.org/10.1109/IGARSS46834.2022.9883922

In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented.Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events.The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.