1. Slide 1 Implementation 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 sensor K. Lenhard, M. Damm, P. Gege

2. Slide 2 Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009 Motivation Large errors observed with ROSIS Target dependence of stray light! Vicarious calibration can not work. Correction algorithm used for regular spectrographs adapted to our needs Comparison: SeaWiFS experiences stray light ~ 10%

3. Slide 3 Implementation of algorithmic correction of stray light in a pushbroom hyperspectral sensor > Karim Lenhard > 17.3.2009 Correction algorithm 1 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 S meas =C∙S in Solve for S in : S in =C -1 ∙S meas 1: Zong et al., Applied Optics, Vol. 45, No. 6 (2006)

4. Slide 4 Implementation 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

5. Slide 5 Implementation 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

6. Slide 6 Implementation 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

7. Slide 7 Implementation 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

8. Slide 8 Implementation 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% ! →

9. Slide 9 Implementation 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) 2 x(pixels) 2 ≈5x10 9 entries computationally challenging → image sharpening algorithms Reprocessing of ROSIS data would lead to different radiances and therefore to different results.

10. Slide 10 Implementation 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