SUNScholar will be unavailable on 25 April 2018 from 08:00 to 10:00 South African Time for routine maintenance. Please contact for queries.

On-board image quality assessment for a satellite

Marais, Izak van Zyl (2009-03)

Thesis (PhD (Electronic Engineering))--University of Stellenbosch, 2009.


The downloading of images is a bottleneck in the image acquisition chain for low earth orbit, remote sensing satellites. An on-board image quality assessment system could optimise use of available downlink time by prioritising images for download, based on their quality. An image quality assessment system based on measuring image degradations is proposed. Algorithms for estimating degradations are investigated. The degradation types considered are cloud cover, additive sensor noise and the defocus extent of the telescope. For cloud detection, the novel application of heteroscedastic discriminant analysis resulted in better performance than comparable dimension reducing transforms from remote sensing literature. A region growing method, which was previously used on-board a micro-satellite for cloud cover estimation, is critically evaluated and compared to commonly used thresholding. The thresholding method is recommended. A remote sensing noise estimation algorithm is compared to a noise estimation algorithm based on image pyramids. The image pyramid algorithm is recommended. It is adapted, which results in smaller errors. A novel angular spectral smoothing method for increasing the robustness of spectral based, direct defocus estimation is introduced. Three existing spectral based defocus estimation methods are compared with the angular smoothing method. An image quality assessment model is developed that models the mapping of the three estimated degradation levels to one quality score. A subjective image quality evaluation experiment is conducted, during which more than 18000 independent human judgements are collected. Two quality assessment models, based on neural networks and splines, are tted to this data. The spline model is recommended. The integrated system is evaluated and image quality predictions are shown to correlate well with human quality perception.

Please refer to this item in SUNScholar by using the following persistent URL:
This item appears in the following collections: