Fine process method for Gaofen-3 L1A-level image

doi:10.7523/j.ucas.2021.0005

Abstract

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

CLC Number:

P237

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.

References

[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.