Cross-track Illumination Correction For Hyperspectral Pushbroom Sensors Using Total Variation and Sparsity Regularization
ID:120 Submission ID:190 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:356 Oral Presentation

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

Duration:20min

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

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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
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