Deep-Learning-Based Multiband Signal Fusion for 3-D SAR Superresolution

Smith, Josiah W.; Torlak, Murat

doi:10.1109/taes.2023.3270111

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…In 2023, Smith et al proposed a deep learning-based algorithm called the kR-Net to solve the problem of the multi-band signal fusion for 3-D SAR super-resolution imaging. It can handle complex imaging scenarios with multiple reflectors and outperforms traditional methods, as well as single-domain CNN models [15]. Although 3-D reconstruction technology can recover the 3-D scattering structure of the target and estimate its true height, it is insufficient in monitoring the deformation of the target.…”

Section: Introductionmentioning

confidence: 99%

Research on 4-D Imaging of Holographic SAR Differential Tomography

Jin

Feng

et al. 2023

Remote Sensing

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Holographic synthetic aperture radar tomography (HoloSAR) combines circular synthetic aperture radar (CSAR) and SAR tomography (TomoSAR) to enable a 360° azimuth observation of the considered scene. This imaging mode achieves a high-resolution three-dimensional (3-D) reconstruction across a full 360°. To capture the deformation information of the observed target, this paper first explores the differential HoloSAR imaging mode, which combines the technologies of CSAR and differential TomoSAR (D-TomoSAR). Then, we propose an imaging method based on the orthogonal matching pursuit (OMP) algorithm and a support generalized likelihood ratio (Sup-GLRT), aiming to achieve high-precision multi-dimensional reconstruction of the surveillance area. In addition, a statistical outlier removal (SOR) point cloud filtering technique is applied to enhance the accuracy of the reconstructed point cloud. Finally, this paper presents the detection of vehicle changes in a parking lot based on the 3-D reconstructed results.

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Section: Introductionmentioning

confidence: 99%