New Cloud Detection Algorithm for Multispectral and Hyperspectral Images: Application to ENVISAT/MERIS and PROBA/CHRIS Sensors
This work presents a new methodology that faces the problem of accurate identification of location and abundance of clouds in multispectral images acquired by space-borne sensors working in the visible and near-infrared (VNIR) spectral range. The amount of images acquired over the globe every day by the instruments on board Earth Observation satellites makes inevitable that many of these images present cloud covers. The objective of this work is to develop and validate a method that takes advantage of the high spectral and radiometric resolution, and the specific band locations (e.g. the oxygen band) of present multispectral sensors to increase the cloud detection accuracy. Moreover, the method provides probability and cloud abundance rather than flags, which can be used to describe detected clouds (subpixel coverage, cloud type, height, etc) more accurately.