Abstract

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.

© 2019 Optical Society of America

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