Ship Classification in High-Resolution SAR Images Using Deep Learning of Small Datasets

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 9; p. 2929
• Main Authors: Wang, Yuanyuan, Wang, Chao, Zhang, Hong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.09.2018; MDPI
• Subjects: Algorithms; Classification; convolutional neural networks; Datasets; fine tuning; high-resolution SAR images; Methods; Neural networks; Pattern recognition; Remote sensing; Researchers; ship classification; small datasets; Training; transfer learning; Beijing China; United States > US; China; Germany; ship classification; fine tuning; transfer learning; convolutional neural networks; high-resolution SAR images; small datasets
• DOI: 10.3390/s18092929
• ISSN: 1424-8220

With the capability to automatically learn discriminative features, deep learning has experienced great success in natural images but has rarely been explored for ship classification in high-resolution SAR images due to the training bottleneck caused by the small datasets. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. First, ship chips are constructed from high-resolution SAR images and split into training and validation datasets. Second, a ship classification model is constructed based on very deep convolutional networks (VGG). Then, VGG is pretrained via ImageNet, and fine tuning is utilized to train our model. Six scenes of COSMO-SkyMed images are used to evaluate our proposed model with regard to the classification accuracy. The experimental results reveal that (1) our proposed ship classification model trained by fine tuning achieves more than 95% average classification accuracy, even with 5-cross validation; (2) compared with other models, the ship classification model based on VGG16 achieves at least 2% higher accuracies for classification. These experimental results reveal the effectiveness of our proposed method.