Definition and Validation of Scientific Algorithms for the SEOSAT/Ingenio GPP

Definition and validation of scientific algorithms for the SEOSAT/Ingenio GPP

Eduardo de Miguel1, Raúl Valenzuela2, Teodoro Bernardino2, Verena Rodríguez2, Alberto Pizarro3, Diana de Miguel3 , Severino Fernández1

Definition and validation of scientific algorithms for SEOSAT/Ingenio GPP

ESRI users conference, Madrid, Sept. 2011

1 Área de Teledetección, INTA. Cta. de Ajalvir s/n. 28850 Torrejón de Ardoz, Madrid (España). Correo electrónico: [email protected]

2 GMV Aerospace and Defence, S.A.U. C/ Isaac Newton, 11. P.T.M. Tres Cantos, 28760 Madrid (España).

3 EADS-CASA Espacio. Avenida de Aragón 404, 28022, Madrid (España).

In the framework of the Spanish Earth Observation mission Ingenio, a Ground Processor Prototype is being developed by GMV under contract with EADS-CASA Espacio.

This GPP is part of an end-to-end mission simulator, aimed to: 1) be used for mission performance analysis during development and

verification phases, 2) be the basis for the future operational L1 ground processor, to be

Background (I)



2) be the basis for the future operational L1 ground processor, to be implemented within the Payload Data Ground Segment.

Área de Teledetección - INTA supports GMV in the definition of the GPP scientific algorithms and is responsible for their validation.

Sun-synchronous orbit, 678 km, repetition cycle 49 days.

Twin cameras, FOV ≈2.5º each; total swath 55 km.

4 bands VNIR 10 m GSD; 1 pan band from 0.6 to 0.8 µm, 2.5 m GSD.

Panchromatic band uses a 7 stages TDI.

minimum MTF@Nyquist = 0.11 (PAN).

Spectral separation with filters.

Background (II): Main characteristics of Ingenio



Spectral separation with filters.

Pointing accuracy:

Along track 500 m

Across track 100 m

Onboard pre-processing: equalization and dark signal removal.

Lossy compression applied to scientific TM.

The GPP includes the following tasks within the SEOSat/Ingenio L1 processing chain:

•identification of saturated and bad pixels,•radiometric correction and calibration, •image restoration (denoising and deconvolution), initially only for Pan band (TBC),

Background (III)



•geolocation and estimation of Rational Polynomial Coefficients (RPCs),•basic image classification (land / sea / bright),•quicklooks generation.

The algorithm selected is based on wavelet transform thresholding.

Why wavelets?

1) Noise is white (= equal power for all spatial frequencies),

2) Noise level is known a priori:

-instrumental noise from system characterization and in orbit test

-photon noise from the signal level at each point

L1b processing: denoising



3) Limitations in other noise-removal methods (median filter, Fourier Transform...)

Algorithm configuration.

•nominal wavelet basis: symlet order 4

•up to 5 decomposition levels

•noise estimation at each level from the expected instrumental and photonic noise (3sigma):

Noise (i,j) = 3 * sqrt(DarkN*DarkN + PhotoGain*DC(i,j))DarkN: the instrumental noise in digital counts

L1b processing: denoising



PhotoG: the number of photons mapped into a digital count

• 75% shrinkage of those coeff<noise

• proportional reduction of the other coefficients (soft thresholding)

Important: noise estimation based on theoretical values for DarkN y PhotoG.

The first step has been building a rigorous model of system signal and noise for each spectral band.

This model has been applied to a synthetic scene and to a airborne image acquired and processed by INTA.

Denoising validation



Lref

Lmin

Lmax

Denoising results



Nominal case

Denoising results



PhotoGain effect

Results on natural scene

L1b processing: deconvolution

Inverse filtering using a specific kernel.

The kernel is derived by inverting the expected instrument PSF.

This procedure is a Wiener filtering if noise is negligible.



The expected PSF is an input => must be evaluated continuouisly during the mission

Deconvolution validation

The validation is based on analysis on the results over a synthetic scene and a natural scene.

The synthetic scene is a bar pattern with spatial frequency 0.5 cycles/pixel. The scene is convolved with the expected Ingenio PSF and noise is added. Next, denoising and deconvolution are applied and the final contrast is tested:



SW/SSD <1.1 after denoising and deconvolution => MTF@Nyquist > 0.35.

On the natural scene the procedure is similar, but we look for artifacts and not for a quantitative contrast verification.

Deconvolution results



Deconvolution results



Imagen AHS originalImagen degradada

Wiener

Diff rest - original

Min: -79Max: 82Media: 0.117Desvstd: 1.793

The geolocation procedure is challenging due to:-the accuracy goal requires the use of GCPs,-focal plane with different detectors, each one with a different line of sight

along-track.

Geolocation (I)



-Filtering of raw AOCS data to reduce noise.-Initial geolocation using a rigorous instrument model and the filtered AOCS info.- Automatic selection of GCPs on a reference image (GMV tool) for:

-estimation of thermoelastic bias-estimation of roll and pitch AOCS error per image line by fitting the errors per GCP to a continuous curve.

Overview of the geolocation procedure

Geolocation (IIa)



per GCP to a continuous curve.-Fine-tunned geolocation using the bias and roll-pitch corrections.

Resample of the geolocated pixels on a UTM grid:-sampling interval 2.5 m / 10 m-inverse geolocation using High-order B-spline resampling.

Simultaneously, a "perfect sensor" image (Pleiades way) will be produced. RPCs will be produced for this product.

Geolocation (IIb)

Overview of the geolocation procedure (cont.)



RPCs will be produced for this product.

The algorithms in the GPP shall allow the geo-location accuracy of L1b data wrt the reference ellipsoid to be better than 50 m at 2σ without GCPs.

The GPP shall allow the geo-location accuracy of L1c data wrt a reference map to be ≤ 2.5 m at 1σ with the use of < 20 GCPs

Geolocation validation: which is the requirement?

Geolocation (III)



The sum of the errors due to implementation of the GPP algorithms for the geolocation at level 1C with GCP should be less than 0.1 pixels RMS (0.3 pixels, max. value).

The geolocation algorithm is validated using an independent geolocation model developed at INTA. This is based in the ESA Earth Explorer C libraries (http://earth.esa.int/cfi/)

The geolocation adjustment with the use of GCPs has been validated in two steps:

Geolocation Validation

Geolocation (IV)



-capability to detect enough valid GCPs. => Test passed for nominal situations (the deformation between the raw and reference images is limited, no clouds nor water bodies...).-capability to adjust roll and pitch errors from GCPs offsets => Test passed for nominal scenes, and excluding "extrapolation" segments at the scene borders.

EEtool vs GPP (initial result)

Geolocation (Va)



RMS error for a complete Ingenio scene vs number of GCPs

Geolocation (Vb)



Conclusions

The validation of image restorarion algorithms confirms they are suitablefor the mission. Moreover, they are robust against uncertainties in theparameters involved in the image generation (PSF, DarkNoise,PhotoGain...)

The algorithms selected for image geolocation achieve the requiredaccuracy in nominal conditions.

The validation of the scientific algorithms for different scenarios and



The validation of the scientific algorithms for different scenarios andincluding non-nominal conditions is a challenging task.

The definition, implementation and validation of the GPP algorithms is ajoint work from by many engineers at GMV, EADS CASA Espacio,Astrium SAS and INTA, with the continuous support of ESA Ingenioproject office and CDTI and IMAG (Ingenio Mission Advisory Group).



Technology

Definition and Validation of Scientific Algorithms for the SEOSAT/Ingenio GPP