Fusion of ALOS-2/PALSAR-2 and Sentinel-1 Polarimetric Imagery with Explainable Neural Network-based Confidence Level Estimation for Glacier Surface Classification

Glacier monitoring and classification play a vital role in understanding the impact of climate change. Remote sensing imagery, particularly synthetic aperture radar (SAR) data, offers valuable information for glacier surface classification due to its all-weather and day-and-night imaging capability. This paper proposes an approach that combines dual-pol ALOS-2/PALSAR-2 and dual-pol Sentinel-1 polarimetric SAR imagery using the inverse-mapping explainable neural network for glacier surface classification. The findings indicate improved training and validation accuracies for the combination of the two SAR datasets as compared to their individual training and validation accuracies. This paper also presents a method for determining the confidence levels of the neural network in its own results. The generated confidence maps have the potential to provide valuable insights into the decision-making process of the neural network and can assist in the selection of suitable training inputs for improving the performance of the network.