In this project, we investigate MRF as a model for classifying image pixels into contaminated vs. non-contaminated. We also derive some useful analytical results. Ground images of Earth are collected on a daily basis from many satellites orbiting the Earth. The images from different spectral bands are than combined in different ways and corrected for some geometrical factors. The resulting images are further processed to reveal many useful information about the Earth.
Clouds are by far the most common source of contamination of such images.
Instead, we investigate application of inhomogeneous MRF's to cloud screening types of problems, where one is looking for bright, continuous regions with possible large scale variability. To further quantify performance of inhomogeneous MRF's, we perform experiments on synthetic images. Parameters can be estimated analytically by reducing PL to a polynomial equation. The scheme of parameter estimation combined with ICM algorithm is guaranteed to converge to a local maximum. Inhomogeneous MRF's do not overfit the data compared to homogeneous MRF's.