Document - Testing a SAR-based ship classifier with different loss functions

2024

Conference article Open Access

Testing a SAR-based ship classifier with different loss functions

Awais Ch. M., Reggiannini M., Moroni D.

Loss Functions, Deep Learning, Ship Classification, SAR

This study investigated the influence of six different loss functions on Synthetic Aperture Radar (SAR) ship classification accuracy across two datasets. Kullback-Leibler Divergence Loss emerged with the highest average accuracy (69.5%), followed by L1 Loss (69.12%) and Focal Loss(68.4%). Interestingly, L1 and Focal Loss exhibited contrasting performance across datasets, suggesting potential data-specific suitability for certain functions. These findings highlight the importance of considering data characteristics and task requirements when selecting loss functions to optimize SAR ship classification performance.

Publisher: CNR Edizioni

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BibTeX entry

@inproceedings{oai:iris.cnr.it:20.500.14243/517581,
	title = {Testing a SAR-based ship classifier with different loss functions},
	author = {Awais Ch.  M. and Reggiannini M. and Moroni D.},
	publisher = {CNR Edizioni},
	year = {2024}
}