Evaluation of Post Hoc Uncertainty Quantification Approaches for Flood Detection From SAR Imagery
Deep neural networks are the current state-of-the-art for analysis of remote sensing imagery. While they often provide accurate results, they are usually prone to be overconfident with respect to their predictions. In particular when these predictions are used by human decision makers in high stake...
Saved in:
Main Authors: | Jakob Ludwig, Ronny Hansch |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/11028630/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SAR and Social-Media-Based Change Detection With Dual-Threshold Fusion for Flood Inundation Mapping
by: Heng Huang, et al.
Published: (2025-01-01) -
DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection
by: Zhimin Wang, et al.
Published: (2025-01-01) -
Weakly Supervised Semantic Segmentation of Mangrove Ecosystem Using Sentinel-1 SAR and Deep Convolutional Neural Networks
by: Arsalan Ghorbanian, et al.
Published: (2025-01-01) -
Improved Flood Insights: Diffusion-Based SAR-to-EO Image Translation
by: Minseok Seo, et al.
Published: (2025-07-01) -
VCINet: Visual Cue-Inspired Feature Learning Network for SAR Building Segmentation
by: Mengyu Wang, et al.
Published: (2025-01-01)