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高分三号卫星非同步重复轨道 SAR 干涉处理。

ScanSAR Interferometry of the Gaofen-3 Satellite with Unsynchronized Repeat-Pass Images.

机构信息

College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Changsha 410073, China.

出版信息

Sensors (Basel). 2019 Oct 28;19(21):4689. doi: 10.3390/s19214689.

DOI:10.3390/s19214689
PMID:31661936
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6864435/
Abstract

Gaofen-3 is a Chinese remote sensing satellite with multiple working modes, among which the scanning synthetic aperture radar (ScanSAR) mode is used for wide-swath imaging. synthetic aperture radar (SAR) interferometry in the ScanSAR mode provides the most rapid way to obtain a global digital elevation model (DEM), which can also be realized by Gaofen-3. Gaofen-3 ScanSAR interferometry works in the repeat-pass mode, and image pair non-synchronizations can influence its performance. Non-synchronizations can include differences of burst central times, satellite velocities, and burst durations. Therefore, it is necessary to analyze their influences and improve the interferometric coherence. Meanwhile, interferometric phase compensation and rapid DEM geolocation also need to be considered in interferometric processing. In this paper, interferometric coherence was analyzed in detail, followed by an iterative filtering method, which helped to improve the interferometric performance. Further, a phase compensation method for Gaofen-3 was proposed to compensate for the phase error caused by the unsynchronized azimuth time offset of image pair, and a closed-form solution of DEM geolocation with ground control point (GCP) information was derived. Application of our methods to a pair of Gaofen-3 interferometric images showed that these methods were able to process the images with good accuracy and efficiency. Notably, these analysis and processing methods can also be applied to other SAR satellites in the ScanSAR mode to obtain DEMs with high quality.

摘要

高分三号是一颗具有多种工作模式的中国遥感卫星，其中扫描合成孔径雷达（ScanSAR）模式用于宽幅成像。ScanSAR 模式下的合成孔径雷达（SAR）干涉测量提供了获取全球数字高程模型（DEM）的最快方法，这一方法也可以由高分三号实现。高分三号的 ScanSAR 干涉测量工作在重复轨道模式下，图像对的不同步会影响其性能。不同步包括脉冲中心时间、卫星速度和脉冲持续时间的差异。因此，有必要分析它们的影响并提高干涉相干性。同时，在干涉处理中还需要考虑干涉相位补偿和快速 DEM 地理定位。本文详细分析了干涉相干性，随后提出了一种迭代滤波方法，有助于提高干涉性能。进一步地，提出了一种适用于高分三号的相位补偿方法，用于补偿图像对方位时间偏移不同步引起的相位误差，并推导出了具有地面控制点（GCP）信息的 DEM 地理定位的闭式解。将我们的方法应用于一对高分三号干涉图像，结果表明这些方法能够以较高的精度和效率处理图像。值得注意的是，这些分析和处理方法也可应用于其他 ScanSAR 模式的 SAR 卫星，以获取高质量的 DEM。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/6f80dd482981/sensors-19-04689-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/2125529203c8/sensors-19-04689-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/045906808802/sensors-19-04689-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/ba52dc9888af/sensors-19-04689-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/b2de27d031e8/sensors-19-04689-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/b3671f268633/sensors-19-04689-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/f0240e6f371c/sensors-19-04689-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/46458ec05081/sensors-19-04689-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/a8d785471a09/sensors-19-04689-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/0473477610c9/sensors-19-04689-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/6f80dd482981/sensors-19-04689-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/2125529203c8/sensors-19-04689-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/045906808802/sensors-19-04689-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/ba52dc9888af/sensors-19-04689-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/b2de27d031e8/sensors-19-04689-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/b3671f268633/sensors-19-04689-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/f0240e6f371c/sensors-19-04689-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/46458ec05081/sensors-19-04689-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/a8d785471a09/sensors-19-04689-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/0473477610c9/sensors-19-04689-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/334e/6864435/6f80dd482981/sensors-19-04689-g010.jpg

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引用本文的文献

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本文引用的文献

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The GF-3 SAR Data Processor.高分三号合成孔径雷达数据处理器
Sensors (Basel). 2018 Mar 10;18(3):835. doi: 10.3390/s18030835.
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Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information.基于XGBoost和极化空间信息的高分三号全极化合成孔径雷达图像分类
Sensors (Basel). 2018 Feb 17;18(2):611. doi: 10.3390/s18020611.
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Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.基于海杂波分布分析和深度卷积神经网络的高分三号合成孔径雷达图像舰船检测
Sensors (Basel). 2018 Jan 24;18(2):334. doi: 10.3390/s18020334.
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The SAR Payload Design and Performance for the GF-3 Mission.高分三号卫星任务的合成孔径雷达（SAR）有效载荷设计与性能
Sensors (Basel). 2017 Oct 23;17(10):2419. doi: 10.3390/s17102419.