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Open AccessArticle

Ship Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset

Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(1), 63; https://doi.org/10.3390/s19010063
Received: 19 November 2018 / Revised: 18 December 2018 / Accepted: 20 December 2018 / Published: 24 December 2018
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Techniques and Applications)
Synthetic aperture radar (SAR) as an all-weather method of the remote sensing, now it has been an important tool in oceanographic observations, object tracking, etc. Due to advances in neural networks (NN), researchers started to study SAR ship classification problems with deep learning (DL) in recent years. However, the limited labeled SAR ship data become a bottleneck to train a neural network. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. To solve the problem of over-fitting which often appeared in training small dataset, we proposed a new method of data augmentation and combined it with transfer learning. Based on experiments and tests, the performance is evaluated. The results show that the types of the ships can be classified in high accuracies and reveal the effectiveness of our proposed method. View Full-Text
Keywords: synthetic aperture radar (SAR); convolutional neural networks (CNNs); deep learning (DL); ship classification synthetic aperture radar (SAR); convolutional neural networks (CNNs); deep learning (DL); ship classification
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Lu, C.; Li, W. Ship Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset. Sensors 2019, 19, 63.

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