Abstract
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%.
Highlights
Object classification is a fundamental problem with numerous applications in computer vision and has been extensively studied for visible (VIS) image in the past decades
Researchers found that features learned from convolutional neural network (CNN) are hierarchical in the whole network [12]; that is, the low-level layer features are similar to Gabor filters and color blobs, the middle-level layer features include fine visual details and semantic information, and the high-level layer features are distinctive semantic features
We present a multilayer convolutional feature fusion method for dual-band maritime ship classification by taking advantage of CNN and recursive neural networks (RNNs)
Summary
Object classification is a fundamental problem with numerous applications in computer vision and has been extensively studied for visible (VIS) image in the past decades. Researchers found that features learned from CNN are hierarchical in the whole network [12]; that is, the low-level layer features are similar to Gabor filters and color blobs, the middle-level layer features include fine visual details and semantic information, and the high-level layer features are distinctive semantic features. They demonstrated the generality and specificity of convolutional feature [13]; that is, first-layer features are general to many datasets and tasks, and last-layer features are specific to a particular dataset or task. 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]
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