Michael Eisinger, Kotska Wallace,. Maximilian Sauer, Michael Schmitt, Markus Huchler. Tony Canas, Steve Knight. Arnauld Heliere, Alain Lefebvre.
End-to-End Tests of the Deconvolution Processing for the EarthCARE MSI
Engineering Confidence mode of the MSI VNS camera
Hardware-Setup of the Engineering Confidence Test Campaign:
Early End-to-End testing of the MSI processing performance Why? The European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) are co-operating to develop the EarthCARE satellite mission with the fundamental objective of improving the understanding of the processes involving clouds, aerosols and radiation in the Earth’s atmosphere. Airbus DS has been selected by ESA as the prime contractor for the space segment; the EarthCARE Multispectral Imager (MSI) is developed by Surrey Satellite Technology Ltd. (SSTL). The MSI is relatively compact for a space borne imager. As a consequence, the immediate point-spread function (PSF) of the instrument will be mainly determined by the diffraction caused by the relatively small optical aperture. In order to still achieve a high contrast image, de-convolution processing is applied to remove the impact of diffraction on the PSF. A Lucy-Richardson algorithm has been chosen for this purpose.
Engineering Confidence Model of the MSI VNS camera
Feasibility of this algorithm has been assessed based on simulated data during the design phase, and has been found to be very promising. Nevertheless, the complexity of such an iterative, non-local algorithm is significant, and the processing results can be affected by a number of parameters, such as the image noise or the determination accuracy of the point-spread function.
How? In order to increase confidence in the performance of the deconvolution algorithm specification and implementation, it has been decided to evaluate the L1 performance as soon as possible in the program, by using real images recorded with an engineering confidence model (ECM) of the instrument in combination with an early version of the EarthCARE ground processor prototype (ECGP V1).
A number of dedicated ground support equipment was used to operate the VNS ECM model instead of the single Instrument Control Unit developed for flight. Measurement Data were recorded by the Data Acquisition Processing Block and converted manually into the packet format required by the ground processor.
Software Setup of the EarthCARE ground processor prototype chain:
To generate real image data a scene was provided to the VNS ECM camera by illuminating a metal grating mask (see figures below) with strong contrast from 100% to 0% transmission.
Engineering Confidence Model of the MSI TIR camera
Two out of four channels have been implemented in the VNS model (VIS and the SWIR1)
All three channels of the TIR model have been present, the TIR is a single-detector design
Mechanics, Optics and the detectors were flight-representative
The thermal design was not representative, and Test-EGSE was used instead of the in-flight Instrument Control Unit (ICU)
Grating Mask used for MTF Characterizations
These data has been recorded with the data acquisition unit, manually converted to the instrument source packet format expected by the ECGP, and then processed using the Lucy Richardson algorithm.
The first implementation of the EarthCARE Ground Processor Prototype (ECGP V1) was used for the processing. This already includes the complete algorithms for the MSI processing.
Klaus Kruse, Alexandra Herzog, Thomas Jäger,
Shailen Bharadia, Mat Maher,
Michael Eisinger, Kotska Wallace,
Maximilian Sauer, Michael Schmitt, Markus Huchler
Tony Canas, Steve Knight
Arnauld Heliere, Alain Lefebvre
End-to-End Tests of the Deconvolution Processing for the EarthCARE MSI
Deconvolution and the Modulation Transfer Function:
Processing of the MSI ECM measurement of a Nyquist line pair
With or without aliasing, any imaging instrument with finite sampling distance and point spread function can create different image from two scenes with identical minimum and maximum intensities:
A diffuser target of line pairs at Instrument Nyquist frequency has been imaged by the SWIR1 channel of the VNS camera. A large number of illuminations has been averaged in order to improve image noise The target was moved slowly across the Instrument FoV and tilted by ~2° against the direction of motion, in order to generate measurements with different phasings between scene and pixel array Detector Array, 128 Samples shown
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Time, 64 images shown
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L1 processing
including Deconvolution
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From these images with reduced contrast, deconvolution processing attempts to restore the original information. This can be fully successful for a sinusoidal intensity variation with a less than Nyquist frequency, but will create higher than “real” intensities for the rectangular signal:
The information can be re-ordered into a 1D-plot, with a different sub-sample position per scan time.
Scene data are available as a 2D array of intensities: I(x,y).
The underlying scene definition is one-dimensional: I=sin(2*π*x*fNyq)
The 2D Image contains additional information on phasing due to the scene rotation.
This information can be transferred to a 1D function by a coordination: ξ=x-sin(2°)*y
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The L1 product shows 185% of the ground truth contrast, up from ~55% in the L0 product.
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The assumed sine wave then represents +108% and –8% of integrated energy per sample, instead of the +100% and 0% in the ground truth.
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The difference indicates an underestimation of the instrument MTF by the calibration (e.g. ~0.37 instead of 0.4).
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These higher than “real” signals do contain very accurate information though, integrating the assumed sine wave with the given amplitude over the extend of the spatial sample provides the correct integrated energy :
This is not surprising, the MTF characterisation accuracy was predicted to be about 10%. For the PFM, detailed investigation of L1 processed test scenes could be utilised to iterated the instrument PSF in order to reach a more accurate representation of data near Nyquist frequency.
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Sample Value [a.u.]
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Klaus Kruse, Alexandra Herzog, Thomas Jäger,
Shailen Bharadia, Mat Maher,
Michael Eisinger, Kotska Wallace,
Maximilian Sauer, Michael Schmitt, Markus Huchler
Tony Canas, Steve Knight
Arnauld Heliere, Alain Lefebvre
112
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End-to-End Tests of the Deconvolution Processing for the EarthCARE MSI
Noise amplification in the L1 processing While white noise is comprised of a constant power spectral density function in the spatial frequency domain, the PSD of a natural image always drops with a 1/f function. The example given here shows an image of the Earth around the 0° meridian, with a spatial resolution of 2Km. The assumed SNR of a homogeneous scene is 250. Under these conditions, there is a 1:1:1 relation between correctly imaged content, aliased content and noise at the Nyquist frequency. Deconvolution processing will enhance the signal at this frequency regardless of the physical source, thus the relative impact of aliasing and the (spectrally resolved) SNR will not be affected by the process. For a spatially homogeneous scene though, there is no real signal at higher frequencies, but only noise. As a result, the classically defined SNR will be decreased by the process. +fNyq
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Integrated Scene PSD
Integrated Noise PSD Amplification by Deconvolution (MSI SWIR1)
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29%
4%
~109%
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22%
12%
~132%
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18%
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~149%
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~178%
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Power Spectral Density [a.u.]
Zone
Scene Data Image Data including aliasing White Noise
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spatial fruequency
As the deconvolution algorithm is robust against the use of incorrect PSFs, the processing could be tuned to enhance frequencies up to a threshold only, and suppress the content near Nyquist frequency. Based on the MSI ECM measurements, all the VNS channels meet the MTF requirement (though not the more stringent stray light requirement) without any deconvolution, and the SNR requirement with full deconvolution:
MTF @ Nyquist frequency
VIS channel SWIR1 channel
L1B (Full Deconvolution)
L0
L1B (Full Deconvolution)
> 0.29
~1
2002
~ 800
> 0.25 >0.30
SWIR 1 Requirements
Deconvolution Processing
>500 ~1
> 0.25
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SNR @ High Reference Level
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VIS Requirement
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1129
~ 450 >250
The SNR and MTF performances of the MSI instrument can be traded against each other to best meet the requirements of the L2 algorithms.
Klaus Kruse, Alexandra Herzog, Thomas Jäger,
Shailen Bharadia, Mat Maher,
Michael Eisinger, Kotska Wallace,
Maximilian Sauer, Michael Schmitt, Markus Huchler
Tony Canas, Steve Knight
Arnauld Heliere, Alain Lefebvre
1
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