RaNeRF: Neural 3-D Reconstruction of Space Targets From ISAR Image Sequences

Liu, Afei; Zhang, Shuanghui; Zhang, Chi; Zhi, Shuaifeng; Li, Xiang

doi:10.1109/tgrs.2023.3298067

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…Techniques for 3D geometry reconstruction predicated on ISAR images can be bifurcated into two categories, contingent upon the system distinction: multi-sensor-based ISAR image 3D geometry reconstruction [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] and single-sensor-based 3D geometry reconstruction [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Within the realm of 3D reconstruction employing multiple sensors, interferometric inverse synthetic aperture radar (InISAR) is the most prevalently utilized method.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the accurate extraction and matching of scattering points persist as significant challenges, impacting the effectiveness of 3D reconstruction efforts. Furthermore, radar systems based on single sensors can exploit target rotation characteristics to achieve imaging in specific scenarios [ 26 , 27 , 28 ], and recently, the RaNeRF method has been proposed, employing deep learning techniques for high-quality reconstruction of target images [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

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PP-ISEA: An Efficient Algorithm for High-Resolution Three-Dimensional Geometry Reconstruction of Space Targets Using Limited Inverse Synthetic Aperture Radar Images

Wang,

Zhu,

et al. 2024

Sensors

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As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA’s neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern—coarse and fine—that enhances search efficiency and conserves computational resources. It introduces a novel objective function ‘sorted-energy semi-accumulation’ to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time.

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