高分三号SAR影像L1A级产品精处理方法

doi:10.7523/j.ucas.2021.0005

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (5): 648-657.DOI: 10.7523/j.ucas.2021.0005

• 电子信息与计算机科学 • 上一篇下一篇

高分三号SAR影像L1A级产品精处理方法

方韩康^1,2, 张波¹, 陈卫荣³, 吴樊¹, 王超^1,2

1. 中国科学院空天信息创新研究院中国科学院数字地球重点实验室, 北京 100094;
2. 中国科学院大学资源与环境学院, 北京 100049;
3. 中国资源卫星应用中心, 北京 100094

收稿日期:2020-12-10 修回日期:2021-01-15 发布日期:2021-05-31
通讯作者: 张波
基金资助:
国家自然科学基金重点项目（41930110）资助

Fine process method for Gaofen-3 L1A-level image

FANG Hankang^1,2, ZHANG Bo¹, CHEN Weirong³, WU Fan¹, WANG Chao^1,2

1. CAS Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;
3. China Centre for Resources Satellite Data and Application, Beijing 100094, China

Received:2020-12-10 Revised:2021-01-15 Published:2021-05-31

摘要/Abstract

摘要： L1A级影像产品是中国高分三号SAR卫星产品发布的初级产品形式。为实现后续科学研究、增值应用，提出一套完整的L1A级产品处理流程，用以生成辐射纠正产品、几何纠正产品等高级产品形式。在辐射纠正过程中，为剔除L1A级产品量化过程中存在空值和零值像素对辐射纠正结果造成的统计偏差，基于高分三号等效噪声系数提出改进的辐射纠正方法。在几何纠正中，提出RPC参数反算算法确定影像角点坐标，基于xml元数据文件中提供的轨道方向、视向以及采样间隔参数保证反算方法的稳健性，在几何校正重采样部分引入SAR滤波算子实现等效视数的提升。对使用Envi5.5软件无法完成几何校正的部分高分三号影像，使用本文提出的处理流程可以顺利完成，其处理结果通过与同为C波段的Sentinel-1影像辐射精度，以及Sentinel-2光学影像几何精度的对比，表明本方法对L1A产品进行处理的精确性和可靠性。

关键词: 高分三号, L1A级产品, 辐射纠正, 几何纠正, 等效噪声系数

Abstract: Level one A (L1A) product of Gaofen-3 SAR satellite is the primary image set delivering for customer. This paper presents a complete workflow to facilitate the post-process of GF-3 L1A images for follow-up scientific research or value-added applications, where robust and precise processing is essential to generate the advanced high-level product concerning radiometric correction and geometric correction. Firstly, to eliminate the statistical bias caused by the null and zero pixel values induced in the quantization of the L1A product, an improved radiometric correction formula is derived based on the equivalent noise coefficient of Gaofen-3 images. Then, to determine the coordinates of image corners, an inverse algorithm supported by RPC parameters is proposed for geometric correction. This algorithm is robust by counting on the orbit direction, look direction, and sampling interval provided in an XML metadata file. Finally, a SAR filter operator is introduced into the resampling step of output results to improve the equivalent look number. Experimental results comparing with the radiometric values of a sentinel-1 image and the geometric accuracy of a sentinel-2 optical image, respectively, validate the accuracy and reliability of this method for L1A product processing.

Key words: Gaofen-3, L1A product, radiometric calibration, geometric correction, equalization noise coefficient

中图分类号:

P237

方韩康, 张波, 陈卫荣, 吴樊, 王超. 高分三号SAR影像L1A级产品精处理方法[J]. 中国科学院大学学报, 2022, 39(5): 648-657.

FANG Hankang, ZHANG Bo, CHEN Weirong, WU Fan, WANG Chao. Fine process method for Gaofen-3 L1A-level image[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 648-657.

参考文献

[1] Li J X, Wang C, Wang S G, et al. Gaofen-3 sea ice detection based on deep learning[C]//2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL). November 19-22, 2017, Singapore. IEEE, 2017:933-939.
[2] Hou X Y, Ao W, Xu F. End-to-end automatic ship detection and recognition in high-resolution Gaofen-3 spaceborne SAR images[C]//2019 IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2019). July 28-August 2, 2019, Yokohama, Japan. IEEE, 2019:9486-9489.DOI:10.1109/IGARSS.2019.8900619.
[3] Yin J J, Yang J. Comparison of Gaofen-3 and radarsat-2 data for polarimetric sar image classification[C]//2018 IEEE International Geoscience and Remote Sensing Symposium(IGARSS 2018). July 22-27, 2018, Valencia, Spain. IEEE, 2018:8112-8115.DOI:10.1109/IGARSS.2018.8517623.
[4] Xu L, Zhang H, Wang C, et al. Classification of Chinese GaoFen-3 fully-polarimetric SAR images:initial results[C]//2017 Progress in Electromagnetics Research Symposium-Fall (PIERS-FALL). November 19-22, 2017, Singapore, Singapore. IEEE, 2017:700-705.DOI:10.1109/PIERS-FALL.2017.8293225.
[5] 刘杉, 张风丽, 韦诗莹, 等. 基于极化分解组合的SAR图像视觉优化和建筑物损毁评估[J]. 中国科学院大学学报, 2020, 37(6):750-759.DOI:10.7523/j.issn.2095-6134.2020.06.005.
[6] 闫成章, 刘畅. 基于显著性的SAR图像船舶目标检测方法[J]. 中国科学院大学学报, 2019, 36(3):401-409.DOI:10.7523/j.issn.2095-6134.2019.03.014.
[7] Wang H, Li H M, Lin M S, et al. Calibration of the copolarized backscattering measurements from Gaofen-3 synthetic aperture radar wave mode imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(6):1748-1762.DOI:10.1109/JSTARS.2019.2911922.
[8] Freeman A. SAR calibration:an overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(6):1107-1121.DOI:10.1109/36.193786.
[9] Chang Y L, Li P X, Yang J, et al. Polarimetric calibration and quality assessment of the GF-3 satellite images[J]. Sensors (Basel, Switzerland), 2018, 18(2):403.DOI:10.3390/s18020403.
[10] Chen Q, Li Z, Zhang P, et al. A preliminary evaluation of the GaoFen-3 SAR radiation characteristics in land surface and compared with radarsat-2 and sentinel-1A[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(7):1040-1044.DOI:10.1109/LGRS.2018.2821238.
[11] 张过, 蒋永华, 李立涛, 等. 高分辨率光学/SAR卫星几何辐射定标研究进展[J]. 测绘学报, 2019, 48(12):1604-1623.
[12] 张过, 费文波, 李贞, 等. 用RPC替代星载SAR严密成像几何模型的试验与分析[J]. 测绘学报, 2010, 39(3):264-270.
[13] 张过, 李贞. 基于RPC的TerraSAR-X影像立体定向平差模型[J]. 测绘科学, 2011, 36(6):146-148,120.DOI:10.16251/j.cnki.1009-2307.2011.06.071.
[14] 吕冠南, 唐新明, 艾波, 等. 稀少控制的多平台星载SAR联合几何定标方法[J]. 测绘学报, 2018, 47(7):986-995.
[15] 王哲远, 李元祥, 郁文贤. SAR图像质量评价综述[J]. 遥感信息, 2016, 31(5):1-10.DOI:10.3969/j.issn.1000-3177.2016.05.001.
[16] Clerc S. Sentinel-2 L1C data quality report[EB/OL].(2020-01-06)[2020-12-26].https://sentinel.esa.int/documents/247904/685211/Sentinel-2_L1C_Data_Qual-ity_Report/6ad66f15-48ca-4e65-b304-59ef00b7f0e0?ver-sion=1.64.
[17] 陈明, 张波, 单子力, 等. Radarsat-2 SAR影像两种定位模型精度的对比分析[J]. 遥感信息, 2012, 27(5):14-18,25.DOI:10.3969/j.issn.1000-3177.2012.05.003.

高分三号SAR影像L1A级产品精处理方法

Fine process method for Gaofen-3 L1A-level image

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