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Using Low-Resolution SAR Scattering Features for Ship Classification

Emanuele Salerno

2022IEEE Geoscience and Remote Sensing Letters21 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Classifier (UML)Synthetic aperture radarComputer scienceArtificial intelligenceScatteringRandom forestPattern recognition (psychology)Contextual image classificationTest setRanking (information retrieval)Remote sensingTraining setData setFeature extractionCross-validationSet (abstract data type)Image (mathematics)GeologyPhysicsOpticsProgramming languageSynthetic Aperture Radar (SAR) Applications and TechniquesAdvanced SAR Imaging TechniquesUnderwater Acoustics Research
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