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基于Sentinel-1双极化数据的北极海域假彩色图像合成方法
作者:于皓  田忠翔  李春花 
单位:国家海洋环境预报中心 自然资源部海洋灾害预报技术重点实验室, 北京 100081
关键词:Sentinel-1 双极化 北极 假彩色 直方图均衡化 
分类号:P731.15;P714
出版年·卷·期(页码):2022·39·第五期(60-69)
摘要:
基于北极海域的Sentinel-1双极化数据,有效地结合去噪算法和图像处理技术,提出了一种高质量的RGB假彩色图像制作方法。该方法使用线性回归模型和一种更高效的热噪声去除算法分别对HH和HV极化数据进行入射角校正和热噪声去除;将HH、HV和混合HH、HV得到的数据分别作为蓝色、红色和绿色通道图层,并使用全局直方图均衡化和限制对比度直方图均衡化进行图像增强,获得最终的RGB假彩色图像。该方法合成的RGB假彩色图像减小了入射角和热噪声对图像质量的影响,增强了地物信息特征和图像的可读性,可为海冰信息提取奠定良好的基础。
Based on the Sentinel-1 dual-polarization data in the Arctic, a high-quality method of synthesizing RGB pseudo color images is proposed by effectively combing denoising algorithm and image processing technology. In this method, incidence angle correction is performed on the HH polarization data using a linear regression model, and thermal noise removal is conducted on the HV polarization data using a more efficient algorithm. The HH, HV polarization data and the mixture of HH and HV polarization data are used as blue, red, and green channel layers in the RGB false color image, respectively, and the enhancements of RGB false color composite are performed using global histogram equalization and contrast limited histogram equalization to obtain the final RGB false color image. In the RGB false color images synthesized by this method, the influences of the incidence angle and thermal noise on the images are reduced, and both the features of the ground information and the readability of the image are enhanced. This RGB false color image could lay a good foundation for the extraction of sea ice information.
参考文献:
[1] 秦大河, 周波涛, 效存德.冰冻圈变化及其对中国气候的影响[J].气象学报, 2014, 72(5):869-879. QIN D H, ZHOU B T, XIAO C D. Progress in studies of cryospheric changes and their impacts on climate of China[J]. Acta Meteorologica Sinica, 2014, 72(5):869-879.
[2] PARKINSON C L, DIGIROLAMO N E. New visualizations highlight new information on the contrasting Arctic and Antarctic seaice trends since the late 1970s[J]. Remote Sensing of Environment, 2016, 183:198-204.
[3] SIMMONDS I, LI M Y. Trends and variability in polar sea ice, global atmospheric circulations, and baroclinicity[J]. Annals of the New York Academy of Sciences, 2021, 1504(1):167-186.
[4] 魏立新, 张占海.北极海冰变化特征分析[J].海洋预报, 2007, 24(4):42-48. WEI L X, ZHANG Z H. Analysis of Arctic sea ice variability[J]. Marine Forecasts, 2007, 24(4):42-48.
[5] 李春花, 李明, 赵杰臣, 等.近年北极东北和西北航道开通状况分析[J].海洋学报, 2014, 36(10):33-47. LI C H, LI M, ZHAO J C, et al. Navigable status analysis of Arctic northeast and northwest passage in recent years[J]. Acta Oceanologica Sinica, 2014, 36(10):33-47.
[6] 季青, 董江, 庞小平, 等.北极东北航道夏季海冰冰情与适航性分析[J].船舶力学, 2021, 25(8):991-1000. JI Q, DONG J, PANG X P, et al. Analysis of sea ice conditions and navigability of Arctic northeast passage in summer[J]. Journal of Ship Mechanics, 2021, 25(8):991-1000.
[7] ZAKHVATKINA N, SMIRNOV V, BYCHKOVA I. Satellite SAR data-based sea ice classification:an overview[J]. Geosciences, 2019, 9(4):152.
[8] HONG D B, YANG C S. Automatic discrimination approach of sea ice in the Arctic Ocean using Sentinel-1 extra wide swath dualpolarized SAR data[J]. International Journal of Remote Sensing, 2018, 39(13):4469-4483.
[9] 欧阳伦曦, 李新情, 惠凤鸣, 等.哨兵卫星Sentinel-1A数据特性及应用潜力分析[J].极地研究, 2017, 29(2):286-295. OUYANG L X, LI X Q, HUI F M, et al. Sentinel-1A data products' characteristics and the potential applications[J]. Chinese Journal of Polar Research, 2017, 29(2):286-295.
[10] PARK J W, KOROSOV A A, BABIKER M, et al. Classification of sea ice types in Sentinel-1 synthetic aperture radar images[J]. The Cryosphere, 2020, 14(8):2629-2645.
[11] 崔艳荣, 邹斌, 韩震, 等.卷积神经网络在SAR遥感海冰分类中的应用可行性分析[J].海洋预报, 2019, 36(5):77-85. CUI Y R, ZOU B, HAN Z, et al. Feasibility analysis of convolutional neural networks in remote sensing sea ice classification[J]. Marine Forecasts, 2019, 36(5):77-85.
[12] 朱立先, 惠凤鸣, 张智伦, 等.基于Sentinel-1A/B SAR数据的西北航道海冰分类研究[J].北京师范大学学报(自然科学版), 2019, 55(1):66-76. ZHU L X, HUI F M, ZHANG Z L, et al. Sea ice classification in northwest passage based on Sentinel-1A/B SAR data[J]. Journal of Beijing Normal University (Natural Science), 2019, 55(1):66-76.
[13] WANG Y R, LI X M. Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning[J]. Earth System Science Data, 2021, 13(6):2723-2742.
[14] CHEN S Y, SHOKR M, LI X Q, et al. MYI floes identification based on the texture and shape feature from dual-polarized Sentinel-1 imagery[J]. Remote Sensing, 2020, 12(19):3221.
[15] BOULZE H, KOROSOV A, BRAJARD J. Classification of sea ice types in Sentinel-1 SAR data using convolutional neural networks[J]. Remote Sensing, 2020, 12(13):2165.
[16] LOHSE J, DOULGERIS A P, DIERKING W. Mapping sea-ice types from Sentinel-1 considering the surface-type dependent effect of incidence angle[J]. Annals of Glaciology, 2020, 61(83):260-270.
[17] LI X M, SUN Y, ZHANG Q. Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(4):3040-3053.
[18] PARK J W, KOROSOV A A, BABIKER M, et al. Efficient thermal noise removal for Sentinel-1 TOPSAR cross-polarization channel[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3):1555-1565.
[19] PARK J W, WON J S, KOROSOV A A, et al. Textural noise correction for Sentinel-1 TOPSAR cross-polarization channel images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6):4040-4049.
[20] SUN Y, LI X M. Denoising Sentinel-1 extra-wide mode crosspolarization images over sea ice[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3):2116-2131.
[21] MAKYNEN M P, MANNINEN A T, SIMILA M H, et al. Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(12):2593-2605.
[22] YU P, QIN A K, CLAUSI D A. Feature extraction of dual-pol SAR imagery for sea ice image segmentation[J]. Canadian Journal of Remote Sensing, 2012, 38(3):352-366.
[23] LEIGH S, WANG Z J, CLAUSI D A. Automated ice-water classification using dual polarization SAR satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(9):5529-5539.
[24] MÄKYNEN M, KARVONEN J. Incidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR imagery over the Kara Sea[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11):6170-6181.
[25] MURASHKIN D, SPREEN G, HUNTEMANN M, et al. Method for detection of leads from Sentinel-1 SAR images[J]. Annals of Glaciology, 2018, 59(76pt2):124-136.
[26] CHANNAPPAYYA S S, BOVIK A C, HEATH R W JR. Rate bounds on SSIM index of quantized images[J]. IEEE Transactions on Image Processing, 2008, 17(9):1624-1639.
[27] CHEN G H, YANG C L, XIE S L. Gradient-based structural similarity for image quality assessment[C]//Proceedings of 2006 International Conference on Image Processing. Atlanta:IEEE, 2006:2929-2932.
[28] PIZER S M, AMBURN E P, AUSTIN J D, et al. Adaptive histogram equalization and its variations[J]. Computer Vision, Graphics, and Image Processing, 1987, 39(3):355-368.
[29] REZA A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 2004, 38(1):35-44.
[30] SETIAWAN A W, MENGKO T R, SANTOSO O S, et al. Color retinal image enhancement using CLAHE[C]//Proceedings of International Conference on ICT for Smart Society. Jakarta:IEEE, 2013:1-3.
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