Scaling Uncertainty Quantification From Patches to Scenes Through Discontinuity-Aware Stitching

Reconstructing spatially continuous 2-D fields out of their individually derived building blocks typically introduces artifacts that decrease the overall perceptual quality of the field. Machine learning (ML) applications encounter such a challenge when patching a U-net-like architecture output. Num...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
Hauptverfasser:	Steckler, Stephen, Orescanin, Marko, Powell, Scott W., Ortiz, Pedro, Petkovic, Veljko
Format:	Artikel
Sprache:	eng
Schlagworte:	Aggregation Bayes methods Bayesian analysis Bayesian deep learning (BDL) Bayesian theory BNN Deep learning Geospatial analysis Gradients Image quality Machine learning microwave remote sensing precipitation Predictive models Probabilistic logic Probability theory Radiance Scaling Stitching Task analysis U-Net Uncertainty Variance
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