Cloud screening algorithm for meris and chris multispectral sensors

Résumé

Earth Observation systems monitor our Planet by measuring, at different wavelengths, the electromagnetic radiation that is reflected by the surface, crosses the atmosphere, and reaches the sensor at the satellite platform, In this process, clouds are one of the most important components of the Earth's atmosphere affecting the quality of the measured electromagnetic signal and, consequently, the properties retrieved from these signals. This Thesis faces the challenging problem of cloud screening in multispectral and hyperspectral images acquired by space-borne sensors working in the visible and near-infrared range of the electromagnetic spectrum. The main objective is to provide new operational cloud screening tools for the derivation of cloud location maps from these sensors' data. Moreover, the method must provide cloud abundance maps -instead of a binary classification- to better describe clouds (abundance, type, height, subpixel coverage), thus allowing the retrieval of surface biophysical parameters from satellite data acquired over land and ocean. In this context, this Thesis is intended to support the growing interest of the scientific community in two multispectral sensors on board two satellites of the European Space Agency (ESA). The first one is the MEdium Resolution Imaging Spectrometer (MERIS), placed on board the biggest environmental satellite ever launched, ENVISAT. The second one is the Compact High Resolution Imaging Spectrometer (CHRIS) hyperspectral instrument, mounted on board the technology demonstration mission PROBA (Project for On-Board Autonomy). The proposed cloud screening algorithm takes advantage of the high spectral and radiometric resolution of MERIS, and of the high number of spectral bands of CHRIS, as well as the specific location of some bands (e.g., oxygen and water vapor absorption bands) to increase the cloud detection accuracy. To attain this objective, advanced pattern recognition and machine learning techniques to detect clouds are specifically developed in the frame of this Thesis. First, a feature extraction based on meaningful physical facts is carried out in order to provide informative inputs to the algorithms. Then, the cloud screening algorithm is conceived trying to make use of the wealth of unlabeled samples in Earth Observation images, and thus unsupervised and semi-supervised learning methods are explored. Results show that applying unsupervised clustering methods over the whole image allows us to take advantage of the wealth of information and the high degree of spatial and spectral correlation of the image pixels, while semi-supervised learning methods offer the opportunity of exploiting also the available labeled samples.