TY - GEN
T1 - Cross-track illumination correction for hyperspectral pushbroom sensors using total variation and sparsity regularization
AU - Zhuang, Lina
AU - Ng, Kwok Po
N1 - Funding Information:
This work was supported in part by the HKBU Start-up Grant 21.4551.162562 and the HKRGC GRF 12306616, 12200317, 12300218 and 12300519.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
KW - Hyperspectral denoising
KW - Hyperspectral destriping
KW - Smile effect
KW - Spectral smile correction
UR - http://www.scopus.com/inward/record.url?scp=85092468218&partnerID=8YFLogxK
U2 - 10.1109/SAM48682.2020.9104285
DO - 10.1109/SAM48682.2020.9104285
M3 - Conference proceeding
AN - SCOPUS:85092468218
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
BT - 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
PB - IEEE Computer Society
T2 - 11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
Y2 - 8 June 2020 through 11 June 2020
ER -