Cross-track Illumination Correction For Hyperspectral Pushbroom Sensors Using Total Variation and Sparsity Regularization
编号:120 访问权限:仅限参会人 更新:2020-08-05 10:17:28 浏览:358次 口头报告

报告开始:2020年06月08日 15:40(Asia/Shanghai)

报告时间:20min

所在会场:[S] Special Session [SS13] Unsupervised Computing And Large-Scale Optimization For Multi-Dimensional Data Processing

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摘要
Cross-track illumination error exists in hyperspectral pushbroom sensor, who scan objects line-by-line with a detector array. When the illumination sensitivity of the individual detectors is not aligned well, or some detectors are degraded/aged, acquired images show non-uniform illumination in the cross-track direction. Meanwhile, because of the line-by-line scanning scheme, the cross-track illumination error is replicated along the flying track. Considering the structure of illumination error cross/along the track, we propose a column (along-track) mean compensation approach with total variation and sparsity regularization (COMCO-TVS), which corrects the illumination via exploiting characteristics of column-mean pixels and column-mean illumination errors: piecewise smoothness and sparsity, respectively, in the spatial-spectral domain. The correction effectiveness of the proposed method is illustrated using semi-real data.
关键词
Hyperspectral imaging
报告人
Lina Zhuang
Hong Kong Baptist University, Hong Kong

稿件作者
Lina Zhuang Hong Kong Baptist University, Hong Kong
Michael Ng University of Hong Kong, Hong Kong
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重要日期
  • 会议日期

    06月08日

    2020

    06月11日

    2020

  • 01月12日 2020

    初稿截稿日期

  • 04月15日 2020

    提前注册日期

  • 12月31日 2020

    注册截止日期

主办单位
IEEE Signal Processing Society
承办单位
Zhejiang University
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