A flood mapping algorithm from cloud contaminated MODIS time-series data using a Markov random field model

This paper addresses the problem of flood detection from the cloud-contaminated MODIS time-series data. Although MODIS data can provide almost daily coverage over the large area with the medium resolution. The use of MODIS data for flood mapping in the tropical regions is a challenging task due to the cloud contamination. Since the floods usually occur in the connected regions over a certain period of time, we employed the Markov random field model to characterize this property. For our experiments, the classification accuracy of flooded and non-flooded areas can be significantly increased by incorporating the MRF model.