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

Abstract EN

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%.