Construction of an Image Quality Assessment Model for Use on Board an Leo Satellite

Download bandwidth is often a limiting factor in the remote sensing image acquisition chain. An onboard image quality assessment system could optimise use of available downlink time by prioritising high quality images for download. An image quality assessment model was developed based on 3 degradation features: cloud cover, additive noise and the defocus extent of the telescope. For training data more than 18000 subjective evaluations of 484 unique degraded images were collected over the Internet. A neural network model and compound spline model were trained and compared. The spline model achieved a linear correlation coefficient of 96.5%.

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