Uncertainty Estimation in SAR-Based Flood Mapping Via Density-Aware Deep Neural Networks

Authors

Li Y., Matgen P., Chini M.

Reference

International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1760-1763, 2024

Description

Deep neural networks (DNNs) have demonstrated remarkable success across various domains, including Earth Observation applications. Despite their achievements, DNNs do not quantify the uncertainty of their predictions, which is particularly crucial for high-stakes applications such as flood mapping. We applied density-aware deep neural networks for uncertainty quantification in SAR-based flood mapping through a single forward pass. The aleatoric uncertainty is captured through softmax entropy, while epistemic uncertainty is quantified using density in the latent feature space. Our image segmentation results illustrate that the employed density-aware deep neural networks exhibit good performance in uncertainty quantification, surpassing Deep Ensembles for out-of-distribution (OOD) data detection.

Link

doi:10.1109/IGARSS53475.2024.10642222