Ship Classification in Remote Sensing Images using FastAI

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dc.contributor.authorRoungroongsom, Chittra
dc.contributor.authorChitsobhuk, Orachat
dc.date.accessioned2026-08-06T10:30:30Z
dc.date.available2026-08-06T10:30:30Z
dc.date.issued2021-01-01
dc.description.abstractSpecifying ship categories in waterways plays an important role in the field of marine surveillance, especially when classification is performed from satellite images due to the advancement in remote sensing technologies. In this paper, we presented an approach for ship classification of optical remote sensing images. Our approach was based on two aspects, modifying models and applying additional techniques to improve accuracy of classification. Two pretrained models, MobileNetV2 and DenseNet121, were modified in this work and all techniques were implemented using Fastai library. To illustrate the effectiveness of our approach, we compared the accuracy of the modified models to the original one. A public Dataset for Ship Classification in Remote sensing images (DSCR), containing six military ship types and a civilian ship type, was used for evaluation. The results showed that our modified DenseNet121 achieved the best accuracy at 99.52% and also outperformed the benchmark result of ResNet101 reported from the original dataset.
dc.identifier.citationProceedings International Conference on Knowledge and Systems Engineering Kse, 2021-November, 2021
dc.identifier.doi10.1109/KSE53942.2021.9648787
dc.identifier.issn26944804
dc.identifier.other2-s2.0-85146201325
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11474
dc.sourceProceedings International Conference on Knowledge and Systems Engineering Kse
dc.subjectconvolutional neural network (CNN)
dc.subjectfastai
dc.subjectremote sensing image (RSI)
dc.subjectship classification
dc.titleShip Classification in Remote Sensing Images using FastAI
dc.typeConference Paper

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