Document - Using low-resolution SAR scattering features for ship classification

2022

Journal article Open Access

Using low-resolution SAR scattering features for ship classification

Salerno E.

Low-resolution synthetic aperture radar (SAR) ship classification Random forests Scattering attributes

This letter reports an experimental study aimed at establishing the questionable usefulness of scattering attributes for ship classification from moderate-resolution SAR images. About 2700 example images representing four ship types have been extracted from the OpenSARShip annotated data set and used to form the training and test sets for random forest models. After importance ranking and cross-validation, different subsets of both geometric and scattering attributes were selected from a fixed training set and used to train the classifier. The results from the validation using the test sets show that the scattering attributes give a significant contribution in terms of overall classification accuracy.

Source: IEEE geoscience and remote sensing letters (Online) 19 (2022). doi:10.1109/LGRS.2022.3183622

Publisher: Institute of Electrical and Electronics Engineers,, Piscataway, NJ , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:468769,
	title = {Using low-resolution SAR scattering features for ship classification},
	author = {Salerno E.},
	publisher = {Institute of Electrical and Electronics Engineers,, Piscataway, NJ , Stati Uniti d'America},
	doi = {10.1109/lgrs.2022.3183622},
	journal = {IEEE geoscience and remote sensing letters (Online)},
	volume = {19},
	year = {2022}
}