Cross-track Illumination Correction For Hyperspectral Pushbroom Sensors Using Total Variation and Sparsity Regularization
ID:120 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:374 Oral Presentation

Start Time:2020-06-08 15:40(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS13Unsupervised Computing And Large-Scale Optimization For Multi-Dimensional Data Processing

Video No Permission

Tips: Only the registered participant can access the file. Please sign in first.

Abstract
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.
Keywords
Hyperspectral imaging
Speaker
Lina Zhuang
Hong Kong Baptist University, Hong Kong

Submission Author
Lina Zhuang Hong Kong Baptist University, Hong Kong
Michael Ng University of Hong Kong, Hong Kong
Submit Comment
Verify Code Change Another
All Comments
Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Contact Information