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自然资源遥感  2023, Vol. 35 Issue (3): 71-79    DOI: 10.6046/zrzyyg.2022161
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于间隔采样的快速变分条带噪声检测方法
白玉川(), 徐锐, 李宗睿, 潘俊()
武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
Fast variational detection of stripe noise based on interval sampling
BAI Yuchuan(), XU Rui, LI Zongrui, PAN Jun()
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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摘要 

为了提高目前主流卫星推扫成像过程中多列条带噪声的检测效率,提出了一种基于间隔采样的快速变分条带噪声检测方法。该方法以条带噪声成分变分建模和优化求解为基础,通过间隔采样和构建带间隔采样参数的条带噪声成分估计模型,完成条带噪声成分的快速求解,然后对条带噪声成分列均值进行一元离群点检测和后处理,完成条带噪声的定位。由于采用间隔采样的策略,该方法在不损失条带噪声检测精度的情况下显著提高了检测效率。

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白玉川
徐锐
李宗睿
潘俊
关键词 条带噪声变分模型自动检测    
Abstract

This study proposed a fast variational detection method for stripe noise based on interval sampling, aiming to improve the detection efficiency of multi-column strip noise during the pushbroom imaging of mainstream satellites. Based on the variational modeling of stripe noise components and the optimal solution, this method can quickly determine stripe noise components through interval sampling and establishing an estimation model of stripe noise components with interval sampling parameters. Then, this method can locate the stripe noise through the one-dimensional outlier detection and post-processing of the column mean values of stripe noise components. Owing to the interval sampling strategy, the method proposed in this study significantly improves the detection efficiency without impairing the stripe noise detection accuracy.

Key wordsstrip noise    variational model    automatic detection
收稿日期: 2022-04-22      出版日期: 2023-09-19
ZTFLH:  TP79  
基金资助:国家自然科学基金重大项目“遥感再分析与智慧服务”(42090011)
通讯作者: 潘 俊(1979-),男,研究员,主要从事计算机视觉、遥感影像处理及应用研究。Email: panjun1215@whu.edu.cn
作者简介: 白玉川(1997-),男,硕士研究生,主要从事遥感影像处理。Email: 2020206190041@whu.edu.cn
引用本文:   
白玉川, 徐锐, 李宗睿, 潘俊. 基于间隔采样的快速变分条带噪声检测方法[J]. 自然资源遥感, 2023, 35(3): 71-79.
BAI Yuchuan, XU Rui, LI Zongrui, PAN Jun. Fast variational detection of stripe noise based on interval sampling. Remote Sensing for Natural Resources, 2023, 35(3): 71-79.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022161      或      https://www.gtzyyg.com/CN/Y2023/V35/I3/71
Fig.1  按照条带模式分类的条带噪声
Fig.2  基于间隔采样的快速变分条带噪声检测方法流程
Fig.3  基于统计的Terra MODIS第33波段去条带结果[19]
Fig.4  不含条带噪声遥感影像的条带噪声成分列均值直方图
Fig.5  条带噪声遥感影像
遥感影像 条带噪声成
分估计模型
影像大
小/像素
条带噪声列
检测结果(列号)
4倍高斯金字塔下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 1 863×5 700 5 669,5 677~5 682
4倍均值下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 1 863×5 700 5 669,5 677~5 682
4倍间隔下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 1 863×5 700 5 677~5 681
4倍间隔下采样遥感影像 带间隔采样参数的条带噪声成分估计模型 1 863×5 700 5 675~5 683
32倍高斯金字塔下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 233×5 700 5 678~5 680
32倍均值下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 233×5 700 5 669,5 678~5 679
32倍间隔下采样遥感影像 基于群组稀疏性的条带噪声成分估计模型 233×5 700 5 678~5 680
32倍间隔下采样遥感影像 带间隔采样参数的条带噪声成分估计模型 233×5 700 5 677~5 680
Tab.1  不同下采样方法条带噪声检测实验结果
Fig.6  条带噪声遥感影像
下采样
倍数
影像大小/像素 计算
时间/s
检测结果(列号)
1 7 450×5 700 3 897 5 538~5 553,5 688~5 692
5 1 490×5 700 91 5 538~5 554,5 687~5 690
10 745×5 700 38 5 539~5 553,5 687~5 690
15 497×5 700 21 5 540~5 552,5 687~5 690
20 373×5 700 15 5 542~5 551,5 689~5 690
25 298×5 700 10 5 542~5 551,5 688~5 690
30 249×5 700 9 5 542~5 551,5 688~5 690
Tab.2  不同下采样倍数条带噪声检测实验结果
Fig.7  不含条带噪声的遥感影像
检测方法 T P T N F P F N e r r
基于间隔采样的快速变分条带噪声检测方法 0 5 700 0 0 0
自适应条带噪声检测方法 0 4 796 904 0 0.159
基于滑动窗口的多列条带噪声检测方法 0 4 042 1 658 0 0.291
Tab.3  不含条带噪声的遥感影像条带噪声检测结果
Fig.8  条带噪声遥感影像
检测方法 T P T N F P F N p r F 1
基于间隔采样的快速变分条带噪声检测方法 6 5 693 0 1 1.000 0.857 0.923
自适应条带噪声检测方法 3 5 692 1 4 0.750 0.429 0.545
基于滑动窗口的多列条带噪声检测方法 4 5 653 40 3 0.091 0.571 0.157
Tab.4  条带噪声遥感影像条带噪声检测结果
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