GENERATING 3D POINT CLOUDS FROM A SINGLE SAR IMAGE USING 3D RECONSTRUCTION NETWORK

被引：0

作者：

Peng, Lingxiao ^{[1
]}

Qiu, Xiaolan ^{[1
,2
]}

Ding, Chibiao ^{[2
]}

Tie, Wenjie ^{[1
]}

机构：

[1] Chinese Acad Sci, Suzhou Inst, Inst Elect, Beijing, Peoples R China

[2] Chinese Acad Sci, Inst Elect, Beijing, Peoples R China

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

Deep learning; 3D reconstruction; generative adversarial networks; synthetic aperture radar (SAR);

D O I：

10.1109/igarss.2019.8900449

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Obtaining the three-dimensional data of the target is very useful for the interpretation and application of the SAR target. This paper proposes a deep learning framework to recover the three-dimensional structure of the target from a single SAR image, which is expressed in the form of 3D point cloud. Due to the small data set of SAR images, the network is combined by two parts. First, the two-dimensional image in the optical perspective is predicted from the SAR target image, and then the 3D points of the target is reconstructed based on the pre-trained 3D reconstruction network model from the optical images. The experiment is based on the MSTAR datasets. The results confirm the effectiveness of the three-dimensional reconstruction method.

引用

页码：3685 / 3688

页数：4

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