Polarimetric imaging detection using a convolutional neural network with three-dimensional and two-dimensional convolutional layers
APPLIED OPTICS(2020)
摘要
Polarimetric imaging detection is a relatively new and largely undeveloped field. Although convolutional neural networks (CNNs) have achieved great success in two-dimensional (2D) normal intensity images in the field of target detection, traditional CNN methods have not been widely applied to optical polarimetric images, and they cannot take full advantage of the connection between different polarimetric images. To solve this problem, three-dimensional (3D) convolutions are adopted to consider the relationship between S0, S1, and S2 images as a third dimension. Based on the 3D convolutions, a CNN with 3D and 2D convolutional layers is introduced to further improve the success rate of target detection with limited polarimetric images. The evaluations in different natural backgrounds reveal that the proposed method achieves higher detection accuracy than that of two traditional methods for comparison. (C) 2019 Optical Society of America
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要