Application of Transfer Learning for Fine-Grained Vessel Classification Using a Limited Dataset

Miličević, Mario; Žubrinić, Krunoslav; Sjekavica, Tomo

doi:10.1007/978-3-030-21507-1_19

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, large-scale datasets like ImageNet are expensive or difficult to collect and timeconsuming to train in practical maritime applications. Thus, in order to improve performance for various practical tasks, such as ship classification, the well-known pretrained CNN models like AlexNet and VGGNet have been widely used to fine-tune on ship image [14][15][16] and extract meaningful ship features [17,18]. Shi et al [19] combined low-level features obtained by Gabor filter and multiscale completed local binary patterns (MS-CLBP) with high-level features extracted from the pretrained CNN model with fine-tuning and classified ship categories on VIS images.…”

Section: Introductionmentioning

confidence: 99%

Dual-Band Maritime Imagery Ship Classification Based on Multilayer Convolutional Feature Fusion

Qiu

Lin

et al. 2020

Journal of Sensors

View full text Add to dashboard Cite

Addressing to the problems of few annotated samples and low-quality fused feature in visible and infrared dual-band maritime ship classification, this paper leverages hierarchical features of deep convolutional neural network to propose a dual-band maritime ship classification method based on multilayer convolutional feature fusion. Firstly, the VGGNet model pretrained on the ImageNet dataset is fine-tuned to capture semantic information of the specific dual-band ship dataset. Secondly, the pretrained and fine-tuned VGGNet models are used to extract low-level, middle-level, and high-level convolutional features of each band image, and a number of improved recursive neural networks with random weights are exploited to reduce feature dimension and learn feature representation. Thirdly, to improve the quality of feature fusion, multilevel and multilayer convolutional features of dual-band images are concatenated to fuse hierarchical information and spectral information. Finally, the fused feature vector is fed into a linear support vector machine for dual-band maritime ship category recognition. Experimental results on the public dual-band maritime ship dataset show that multilayer convolution feature fusion outperforms single-layer convolution feature by about 2% mean per-class classification accuracy for single-band image, dual-band images perform better than single-band image by about 2.3%, and the proposed method achieves the best accuracy of 89.4%, which is higher than the state-of-the-art method by 1.2%.

show abstract