A Point Clouds Framework for 3-D Reconstruction of SAR Images Based on 3-D Parametric Electromagnetic Part Model

• Main Authors: Yang, Zhi-Long, Zhou, Ruo-Yi, Wang, Feng, Xu, Feng
• Format: Conference Proceeding
• Language: English
• Subjects: 3-D reconstruction; deep learning; Electromagnetic scattering; Feature extraction; Generative adversarial networks; parametric electromagnetic part model; point clouds generation; Radar polarimetry; SAR; Solid modeling; Three-dimensional displays; Training
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS47720.2021.9553789

3-D reconstruction is a hot topic in remote sensing as well as computer vision. The particularity and complexity of the microwave scattering mechanism bring great challenges to the 3D reconstruction of SAR images, and the applicability of existing methods need to be improved. This study proposes an efficient and explainable point clouds framework for three-dimensional reconstruction of SAR images based on three-dimensional parametric electromagnetic part models. This 3-D SAR reconstruction framework consists of two parts: a feature extraction generative adversarial network and a 3-D reconstruction generative network. The feature extraction generative adversarial network has 5 convolutional layers to extract the features of single SAR image and save them in the form of graph, then input this graph to the 3-D reconstruction generative network and we can get the main shape of the target from a SAR image. This framework effectively reduces the numbers of observation for 3-D reconstruction and make the 3-D reconstruction from single SAR image possible.