A Novel Target Extraction and Energy-Balancing Method for HoloSAR 3D Imaging

Holographic synthetic aperture radar (HoloSAR) enables 360° three-dimensional reconstruction by incoherently stacking tomographic subaperture images. However, after conventional subaperture-wise TomoSAR reconstruction and non-coherent integration, the resulting 3D imagery suffers from severe dynamic range imbalance due to angle-dependent scattering responses: wide-angle strong scatterers are repeatedly amplified, whereas narrow-angle weak structures are buried below the noise floor. To address this post-processing challenge, we propose a joint statistical filtering framework operating on the reconstructed subaperture-domain 3D images that fuses the coefficient of variation, inter-subaperture correlation, and spectral entropy with adaptive discriminative-power weighting; target screening is then performed via a Gaussian mixture model-based Bayesian optimal threshold. For pixels classified as weak targets, a percentile-matching energy-balancing transformation is applied to adaptively rescale their energy to the main-target reference level while preserving relative amplitude relationships. Experiments on real-world Ku-band UAV circular SAR data demonstrate that the proposed method effectively compresses the dynamic range, suppresses background noise, and recovers weak narrow-angle structures that are lost in traditional non-coherent superposition, yielding more complete and interpretable HoloSAR 3D reconstructions. Quantitative evaluation on Ku-band UAV circular SAR data demonstrates that the proposed method improves the Target-to-Background Ratio by 0.7 dB (to 11.2 dB), achieves a Background Suppression Ratio of −5.2 dB, increases the Structural Completeness Index by 156% (to 1428.1), and compresses the original dynamic range imbalance, which exceeds 50 dB, while preserving scene physical realism (ENL ≈ 7.4 × 10−3).