Neural Network Model for Multi-Sensor Fusion and Inverse Mapping Dynamics for the Analysis of Significant Factors

With the rise in the number of remote-sensing satellites in the past decade, interest has been drawn towards the fusion of satellite data. This study investigates the fusion of features obtained from the L-band ALOS-2 Synthetic aperture radar (SAR) and the Sentinel-2 optical satellite by the use of neural networks. It identifies the prominent features by using an inverse-mapping algorithm. The fusion results show an increased classification accuracy compared to that of independent sensors. The algorithms have been tested on the data of the 2018 earthquake in Sulawesi, Indonesia.