A Structurally Flexible Occupancy Network for 3-D Target Reconstruction Using 2-D SAR Images
Driven by deep learning, three-dimensional (3-D) target reconstruction from two-dimensional (2-D) synthetic aperture radar (SAR) images has been developed. However, there is still room for improvement in the reconstruction quality. In this paper, we propose a structurally flexible occupancy network...
Saved in:
Main Authors: | Lingjuan Yu, Jianlong Liu, Miaomiao Liang, Xiangchun Yu, Xiaochun Xie, Hui Bi, Wen Hong |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/2/347 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Three-dimensional reconstruction from computed tomography images of respiratory system in new zealand rabbits
by: Mustafa Orhun Dayan, et al. -
Advancing Pediatric Surgery: The Use of HoloLens 2 for 3D Anatomical Reconstructions in Preoperative Planning
by: Marco Di Mitri, et al.
Published: (2024-12-01) -
Case report: Innovative approach to esophageal cancer with right aortic arch: neoadjuvant immunotherapy and 3D reconstruction
by: Chengwen Luo, et al.
Published: (2025-01-01) -
MRCNN: Multi-input residual convolution neural network for three-dimensional reconstruction of bubble flows from light field images
by: Heng Zhang, et al.
Published: (2025-02-01) -
Computed tomography reconstruction and morphometric analysis of the humerus and femur in new zealand rabbits
by: Muhammet Lutfi Selcuk
Published: (2023-12-01)