计算机科学 ›› 2010, Vol. 37 ›› Issue (5): 265-267.

• 图形图像 • 上一篇    下一篇

一种高光谱图像条带噪声去除改进算法

郑逢斌,支晶晶,高海亮,赖积保,潘伟   

  1. (河南大学数据与知识工程研究所 开封475001);(中国科学院遥感应用研究所 北京100101);(国家航天局航天遥感论证中心 北京100101)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(60973126),国防科工委项目科技三项《HJ-1卫星数据应用研究(07K00100KJ)(2006-2008)》资助。

Improved Destriping Algorithm of Hyperspectral Images

ZHENG Feng-bin,ZHI Jing-jing,GAO Hai-liang,LAI Ji-bao,PAN Wei   

  • Online:2018-12-01 Published:2018-12-01

摘要: 传统的矩匹配方法改变了图像在成像行或列方向的均值分布,使原始图像信息发生了较大改变。在分析HJ-1-A星超光谱图像条带噪声的基础上,提出了一种改进的矩匹配方法,将传统矩匹配算法中“参考图像”的平均值和标准差分别用平滑滤波处理后的列均值和方差来代替。实验结果表明,与传统矩匹配方法相比,该方法能减少图像信息的丢失,并能在保持原始图像特征的前提下有效地去除条带噪声。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。

关键词: 矩匹配,平滑滤波,条带去除,高光谱图像

Abstract: Traditional moment matching algorithm can change the line average or column average of images and cause the original image information change. After analyzing HJ-1-A satellite hyperspectral images,an improved algorithm of Moment Matching was put forward. The proposed algorithm uses the column average and variance which was processed with smooth filter instead of the average and standard deviation of the reference image in traditional moment matching algorithm. Comparing with the traditional algorithm,it has advantages of less image information loss and effectively destriping the strips under the premise of maintaining the original image features. The method has a strong applicability for destriping other multi-sensor remote sensing images.

Key words: Moment matching, Smooth filter, Destriping, Hyperspectral image

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