Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection

Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection

Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. In this study, we used Monte Carlo dropout (MCDO) to characterize the uncertainty of deep learning image cla...

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Bibliographic Details
Main Authors: Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
data fusion
uncertainty quantification
simulated data
deep neural network
synthetic aperture radar
Online Access:https://www.mdpi.com/2072-4292/16/24/4669
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