基于快速自聚焦的多基线干涉相位误差校正方法

王龙祥; 向卫; 王吉利; 张子璇; 王浩泽; 卢虹良; 宋红军

doi:10.11834/jrs.20265358

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基于快速自聚焦的多基线干涉相位误差校正方法
A multi-baseline interferometric phase error calibration method based on fast autofocus
2026年30卷第4期页码：929-942
收稿：2025-09-12，

纸质出版：2026-04-07
DOI： 10.11834/jrs.20265358
稿件说明：

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王龙祥，向卫，王吉利，张子璇，王浩泽，卢虹良，宋红军.2026.基于快速自聚焦的多基线干涉相位误差校正方法.遥感学报，30（4）： 929-942 DOI： 10.11834/jrs.20265358.

Wang L X，Xiang W，Wang J L，Zhang Z X，Wang H Z，Lu H L and Song H J. 2026. A multi-baseline interferometric phase error calibration method based on fast autofocus. National Remote Sensing Bulletin， 30（4）：929-942 DOI： 10.11834/jrs.20265358.

摘要

基于多基线干涉的层析合成孔径雷达TomoSAR（Synthetic Aperture Radar Tomography）通过单天线重复航过或多天线单次航过获取多基线观测数据，在高程向形成第三维合成孔径，从而具备三维成像能力并实现森林三维结构的精细重建。然而，轨道间残余相位误差会引发层析谱的能量弥散与图像模糊，从而严重制约森林高度反演的精度与稳定性。此外，现有自聚焦方法在应对此问题时，常因相位误差中线性分量的干扰，产生不连续的层析剖面，不仅解译难度大，计算效率也较低。本文提出了一种基于子区域的对比度优化快速自聚焦SFCOA（Subarea Fast Contrast Optimization Autofocus）相位校正方法。该方法利用小尺度内相位误差的空间不变性，将传统逐像素优化转化为少量子区域的快速对比度最大化求解，显著降低了优化变量的维度；同时，借助相邻子区间的空间连续性构建链式初始化策略，大幅减少迭代次数与收敛时间。基于塞罕坝林场的机载P波段TomoSAR数据开展实验验证，结果表明所提SFCOA方法在不牺牲层析聚焦质量的前提下，其计算效率显著优于基于逐像元优化的PDCO方法，可将整景数据处理时间由小时级降至秒级。在森林垂直结构重建方面，与PSI、PGA及NC-PGA等方法相比，SFCOA所得层析成像结果的旁瓣水平更低以及主瓣展宽得到有效抑制，其层析功率谱的连续性也显著更优。在森林高度反演中，SFCOA相较于NC-PGA取得了更优的结果，其决定系数（

²）从0.516提升至0.609，平均绝对误差MAE（Mean Absolute Error）、平均绝对百分比误差（Mean Absolute Percentage Error，MAPE）与均方根误差 RMSE（Root Mean Square Error）分别从0.509 m、3.152%、0.627 m降至0.435 m、2.820%、0.526 m。所提出的SFCOA方法展现了效率与精度之间的优越平衡能力，为TomoSAR实现高精度三维森林测绘提供了一种高效的解决方案。

Abstract

Synthetic Aperture Radar Tomography (TomoSAR)

which is based on multi-baseline interferometry

utilizes repeat-pass monostatic or single-pass multistatic data to synthesize an aperture in the elevation direction. This technique enables 3D imaging and the high-resolution reconstruction of forest structures. However

residual phase errors between tracks frequently lead to spectral energy dispersion and image blurring

significantly compromising the accuracy and stability of forest height retrieval. Moreover

existing phase error calibration methods require a high computational load for the tomographic imaging of large-scale forest scenarios.

To address the aforementioned issues

this study proposes a novel phase error calibration method

called Subarea Fast Contrast Optimization Autofocusing (SFCOA)

which enhances the efficiency and accuracy of TomoSAR imaging. This method leverages the spatial invariance of phase errors within local subareas to reduce the dimensionality of the optimization problem. By transforming traditional pixel-by-pixel optimization into an efficient contrast maximization task that is performed over a limited number of subareas

the method significantly lowers computational complexity. In addition

a chain initialization strategy based on spatial continuity between adjacent subareas minimizes the required iterations and substantially accelerates convergence.

The proposed SFCOA method is validated using airborne P-band TomoSAR data acquired over Saihanba Forest in Northern China. SFCOA is then compared with several representative phase calibration methods

including Phase Derivative Constrained Optimization (PDCO)

Phase-Shifting Interferometry (PSI)

Phase Gradient Autofocus (PGA)

and Network Construction (NC)-PGA. After phase error calibration

tomograms are generated using a Capon beamformer

and the resulting vertical profiles are evaluated against LiDAR measurements. The qualitative analysis of representative azimuth lines demonstrates that SFCOA achieves the narrowest main lobes

the lowest side lobes

and smooth vertical continuity in the reconstructed tomograms. By contrast

PDCO results exhibit vertical discontinuities. PSI only partially suppresses interference

with residual side lobes indicating incomplete phase error calibration. PGA achieves reasonable focus but suffers from contrast degradation near segment transitions

blurring the separation between canopy and ground reflections. Although NC-PGA reconstructs a continuous forest vertical structure

it remains inferior to SFCOA in terms of tomographic sharpness and peak localization accuracy.

Quantitative evaluations confirm the computational efficiency of SFCOA

which requires only 12.36 s to process the study area on a 2.5 GHz Intel CPU with 16 GB RAM

compared with 16.7 h for PDCO

388.97 s for PSI

28.25 s for PGA

and 45.39 s for NC-PGA. In forest height estimation

SFCOA also achieves superior accuracy

improving

from 0.516 to 0.609

mean absolute error from 0.509 m to 0.435 m

mean absolute percentage error from 3.152% to 2.820%

and root mean square error from 0.627 m to 0.526 m relative to NC-PGA.

In summary

SFCOA provides an accurate and computationally efficient phase error calibration solution for TomoSAR in forested environments. By combining subarea processing

derivative-constrained contrast optimization

and chain initialization

it achieves an effective balance between processing speed and reconstruction quality. The method exhibits strong potential for supporting high-precision 3D forest mapping and sustainable forest management.

关键词

Keywords

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