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基于改进Kalman滤波模型的扫描合成孔径雷达图像扇贝效应校正方法

蔡永华 王宇 范怀涛

蔡永华, 王宇, 范怀涛. 基于改进Kalman滤波模型的扫描合成孔径雷达图像扇贝效应校正方法[J]. 电子与信息学报, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060
引用本文: 蔡永华, 王宇, 范怀涛. 基于改进Kalman滤波模型的扫描合成孔径雷达图像扇贝效应校正方法[J]. 电子与信息学报, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060
Yonghua CAI, Yu WANG, Huaitao FAN. A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060
Citation: Yonghua CAI, Yu WANG, Huaitao FAN. A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1212-1218. doi: 10.11999/JEIT200060

基于改进Kalman滤波模型的扫描合成孔径雷达图像扇贝效应校正方法

doi: 10.11999/JEIT200060
基金项目: 国家自然科学基金(61901442)
详细信息
    作者简介:

    蔡永华:男,1996年生,博士生,研究方向为星载SAR图像与信号处理

    王宇:男,1980年生,研究员,博士生导师,研究方向为SAR系统设计与信号处理技术,新体制星载SAR技术等

    范怀涛:男,1990年生,副研究员,研究方向为高分辨率宽幅星载SAR成像

    通讯作者:

    蔡永华 caiyonghuanwpu@126.com

  • 中图分类号: TN959.74

A Scalloping Correction Method for ScanSAR Image Based on Improved Kalman Filter Model

Funds: The National Natural Science Foundation of China (61901442)
  • 摘要: 星载扫描合成孔径雷达(ScanSAR)采取Burst工作模式,该模式在获得宽幅测绘能力的同时,也导致图像中产生了固有的扇贝效应,严重影响图像的视觉效果和定量应用。该文结合对ScanSAR图像方位向统计特性的分析,针对现有滤波模型稳定性差和时间复杂度高等缺点,提出了一种改进的Kalman滤波模型,对图像方位向标准差和均值进行滤波以校正扇贝条纹。在高分三号(GF-3)卫星获取的真实ScanSAR图像上的校正结果验证了改进算法的有效性和高效性,此外在建筑群和海陆交界等复杂场景图像上的实验结果表明,改进算法具有较强的鲁棒性。
  • 图  1  ScanSAR工作模式示意图(两子带)

    图  2  GF-3获取的ScanSAR图像

    图  3  方位向均值和标准差分布

    图  4  扇贝效应分布

    图  5  扇贝效应校正流程图

    图  6  各算法校正结果比较

    图  7  多场景下扇贝效应校正效果

    图  8  算法运算时间比较

    表  1  扇贝效应强度量化比较(dB)

    扇贝效应最大值最小值平均值标准差
    校正前2.661.712.010.19
    Iqbal算法0.760.150.460.13
    谷昕炜算法1.630.520.940.21
    本文算法0.480.030.220.11
    下载: 导出CSV

    表  2  Kalman滤波器比较

    校正算法阶数(阶)滤波次数(次)迭代次数(次)
    Iqbal算法2n$m - 1$
    谷昕炜算法1n$m - 1$
    本文算法12$n - 1$
    下载: 导出CSV
  • [1] 张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049

    ZHANG Qingjun. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049
    [2] BAMLER R. Optimum look weighting for burst-mode and ScanSAR processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(3): 722–725. doi: 10.1109/36.387587
    [3] VIGNERON C M. Radiometric image quality improvement of ScanSAR data[D]. [Master dissertation], University of British Columbia, 1996. doi: 10.14288/1.0065108.
    [4] SHIMADA M. Long-term stability of L-band normalized radar cross section of Amazon rainforest using the JERS-1 SAR[J]. Canadian Journal of Remote Sensing, 2005, 31(1): 132–137. doi: 10.5589/m04-058
    [5] SHIMADA M. A new method for correcting ScanSAR scalloping using forests and inter-SCAN banding employing dynamic filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12): 3933–3942. doi: 10.1109/TGRS.2009.2027596
    [6] 张晓, 仇晓兰, 仲利华, 等. 基于系统噪声抑制的ScanSAR辐射校正方法[J]. 国外电子测量技术, 2015, 34(6): 47–51. doi: 10.19652/j.cnki.femt.2015.06.011

    ZHANG Xiao, QIU Xiaolan, ZHONG Lihua, et al. Radiometric correction algorithm for ScanSAR scalloping based on reduction of system noise[J]. Foreign Electronic Measurement Technology, 2015, 34(6): 47–51. doi: 10.19652/j.cnki.femt.2015.06.011
    [7] ROMEISER R, HORSTMANN J, and GRABER H. A new algorithm for descalloping ScanSAR images by post processing[C]. ESA Living Planet Symposium, Bergen, Norway, 2010: 354–358.
    [8] ROMEISER R, HORSTMANN J, CARUSO M J, et al. A descalloping postprocessor for ScanSAR images of ocean scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3259–3272. doi: 10.1109/TGRS.2012.2222648
    [9] SCHIAVULLI D, SORRENTINO A, and MIGLIACCIO M. An innovative technique for postprocessing descalloping[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 10(3): 424–427. doi: 10.1109/LGRS.2012.2207879
    [10] IQBAL M, CHEN Jie, YANG Wei, et al. Kalman filter for removal of scalloping and inter-scan banding in scanSAR images[J]. Progress in Electromagnetics Research, 2012, 132: 443–461. doi: 10.2528/PIER12082107
    [11] IQBAL M and CHEN Jie. Removal of scalloping in ScanSAR images using Kalman filter[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 260–263. doi: 10.1109/IGARSS.2012.6351588.
    [12] 谷昕炜, 杨威, 陈杰. 海陆交界非平稳场景星载ScanSAR扇贝效应抑制方法[J]. 海军航空工程学院学报, 2018, 33(1): 125–129, 134. doi: 10.7682/j.issn.1673-1522.2018.01.005

    GU Xinwei, YANG Wei, and CHEN Jie. Suppression of scalloping effects in spaceborne ScanSAR images for non-stationary scene of coastal area monitoring[J]. Journal of Naval Aeronautical and Astronautical University, 2018, 33(1): 125–129, 134. doi: 10.7682/j.issn.1673-1522.2018.01.005
    [13] CUMMING I G, WONG F H, 洪文, 胡东辉译. 合成孔径雷达成像: 算法与实现[M]. 北京: 电子工业出版社, 2012: 285–286.

    CUMMING I G, WONG F H, HONG Wen, HU Donghui. translation. Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation[M]. Beijing: Publishing House of Electronics Industry, 2012: 285–286.
    [14] PELI E. Contrast in complex images[J]. Journal of the Optical Society of America A, 1990, 7(10): 2032–2040. doi: 10.1364/JOSAA.7.002032
    [15] HAWKINS R K and VACHON P W. Modelling SAR scalloping in burst mode products from RadarSAT-1 and ENVISAT[C]. The CEOS Working Group on Calibration/Validation SAR Workshop, London, United Kingdom, 2002: 24–26.
    [16] 胡炎, 单子力, 高峰. 基于增强指数加权均值比的SAR图像边缘检测算法[J]. 电子与信息学报, 2018, 40(5): 1166–1172. doi: 10.11999/JEIT170806

    HU Yan, SHAN Zili, and GAO Feng. Edge detection algorithm for SAR image based on enhanced ROEWA[J]. Journal of Electronics &Information Technology, 2018, 40(5): 1166–1172. doi: 10.11999/JEIT170806
    [17] 陈祥, 孙俊, 尹奎英, 等. 基于Otsu与海域统计特性的SAR图像海陆分割算法[J]. 数据采集与处理, 2014, 29(4): 603–608. doi: 10.3969/j.issn.1004-9037.2014.04.017

    CHEN Xiang, SUN Jun, YIN Kuiying, et al. Sea-land segmentation algorithm of SAR image based on Otsu method and statistical characteristic of sea area[J]. Journal of Data Acquisition and Processing, 2014, 29(4): 603–608. doi: 10.3969/j.issn.1004-9037.2014.04.017
    [18] CHENG Dongcai, MENG Gaofeng, XIANG Shiming, et al. Efficient sea–land segmentation using seeds learning and edge directed graph cut[J]. Neurocomputing, 2016, 207: 36–47. doi: 10.1016/j.neucom.2016.04.020
    [19] LEI Sen, ZOU Zhengxia, LIU Dunge, et al. Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features[J]. Infrared Physics & Technology, 2018, 91: 12–17. doi: 10.1016/j.infrared.2018.03.012
    [20] 肖乐意, 欧阳红林, 范朝冬. 基于记忆分子动理论优化算法的多目标截面投影Otsu图像分割[J]. 电子与信息学报, 2018, 40(1): 189–199. doi: 10.11999/JEIT170301

    XIAO Leyi, OUYANG Honglin, and FAN Chaodong. Multi-objective cross section projection Otsu’s method based on memory knetic-molecular theory optimization algorithm[J]. Journal of Electronics &Information Technology, 2018, 40(1): 189–199. doi: 10.11999/JEIT170301
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出版历程
  • 收稿日期:  2020-01-15
  • 修回日期:  2020-12-04
  • 网络出版日期:  2020-12-15
  • 刊出日期:  2021-05-18

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