Collaborative identification method for sea battlefield target based on deep convolutional neural networks

Guangdi Zheng; Mingbo Pan; Wei Liu; Xuetong Wu

doi:10.1117/12.2285713

8 March 2018 Collaborative identification method for sea battlefield target based on deep convolutional neural networks

Guangdi Zheng, Mingbo Pan, Wei Liu, Xuetong Wu

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091A (2018) https://doi.org/10.1117/12.2285713
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

The target identification of the sea battlefield is the prerequisite for the judgment of the enemy in the modern naval battle. In this paper, a collaborative identification method based on convolution neural network is proposed to identify the typical targets of sea battlefields. Different from the traditional single-input/single-output identification method, the proposed method constructs a multi-input/single-output co-identification architecture based on optimized convolution neural network and weighted D-S evidence theory. The simulation results show that

Citation Download Citation

Guangdi Zheng, Mingbo Pan, Wei Liu, and Xuetong Wu "Collaborative identification method for sea battlefield target based on deep convolutional neural networks", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091A (8 March 2018); https://doi.org/10.1117/12.2285713

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KEYWORDS

Convolution

Neural networks

Convolutional neural networks

Image fusion

Target recognition

Image processing

Image classification

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