Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation ...

Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics perf...

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Bibliographic Details
Main Authors: de Lima, Rafael Pires, Vahedi, Behzad, Karimzadeh, Morteza
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2023
Subjects:
Computer Vision and Pattern Recognition cs.CV
Image and Video Processing eess.IV
FOS Computer and information sciences
FOS Electrical engineering, electronic engineering, information engineering
Sea ice
Online Access:https://dx.doi.org/10.48550/arxiv.2310.17135
https://arxiv.org/abs/2310.17135
Description
Summary:Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generation of ice maps for large regions. However, results from CNN models still need to undergo scrutiny as higher metrics performance not always translate to adequate outputs. Sea ice type classes are imbalanced, requiring special treatment during training. We evaluate how three different loss functions, some developed for imbalanced class problems, affect the performance of CNN models trained to predict the dominant ice type in Sentinel-1 images. Despite the fact that Dice and Focal loss produce higher metrics, results from cross-entropy seem generally more physically consistent. ...

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