Slide 1Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensorK. Lenhard, M. Damm, P. Gege

Slide 2Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Motivation

Large errors observed with ROSISTarget dependence of stray light! Vicarious calibration can not work.

Correction algorithm used for regular spectrographs adapted to our needsComparison: SeaWiFS experiences stray light ~ 10%

Slide 3Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Correction algorithm1

Basic idea: measure Line Spread Function (LSF)

LSF quantifies how much signal neighbouring detector elements detect if one sensor element is illuminated

ROSIS is a pushbroom sensor → 2D detector array: Geometric and spectral stray light

LSFs can be merged into a matrix C: Each column contains the LSF of one channel/pixel

Smeas=C∙Sin

Solve for Sin:

Sin=C-1 ∙Smeas

1: Zong et al., Applied Optics, Vol. 45, No. 6 (2006)

Slide 4Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Measurement of Spectral Stray Light

Direct measurement of LSF difficult. Radiometric resolution/SNR too low

Used optical band pass filters to cover the bandwidth of ROSIS →higher total irradiance

Slide 5Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Measurement of Spectral Stray Light

Distinguish signal from stray light via treshholds

Estimation of stray light by comparison of expected and actual signal for each filter

Second iteration with corrected estimates

Drawback: large distance between illuminated and corrected channels

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Channel

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STREUSTRAHLUNGSANTEILE | 11.3.2009

Slide 6Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Exemplary Correction

Promising results: Below 430 nm, regular signal is not expected

Large correction in the blue spectrum, as expected

Will be implemented in processing chain

Slide 7Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Measurement of spectral stray light

Saturate channel to observe stray light in adjacing channels

Reduce signal to allow for normalisation

This provides the LSF closer to the originating pixel than the filter approach

Yet to be measured systematically

Slide 8Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Geometric Stray Light

Simulation of error with inverse calculation for actual LSF of ROSIS

„Adjacency effect“ – dark spot surrounded by bright ground

Worst case error ~ 10% !

→

Slide 9Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Additional remarks

Ideally, geometric and spectral stray light have to be corrected simultaneously

Handling of tensor with (channels)2x(pixels)2≈5x109 entries computationally challenging → image sharpening algorithms

Reprocessing of ROSIS data would lead to different radiances and therefore to different results.

Slide 10Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009

Conclusion

Stray light in hyperspectral sensors can lead to large systematic, target-dependent radiometric errors.

Methods presented can be used to quantify amount of stray light

Method shown can help correcting stray light induced errors