Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

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Narrow field-of-view scanning Classes-Events thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO).Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic.Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit.

The current contribution considers a fourth possibility, that of an imager operating in LEO.The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array.At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane.

Presented is a full-field focusing algorithm based on an artificial neural network (ANN).Absorbed power distributions on the microbolometer array produced ULT DIGEST ENZ URGENT CARE by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager.The resulting readout array/scene pairs are then used to train an ANN.

We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene.This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager.