A hidden Markov model method for non-stationary noise reduction: case study on Sentinel data for mowing detection

We propose a method for reducing the non-stationary noise in signal time series of Sentinel data, based on a hidden Markov model. Our method is applied on interferometric coherence from Sentinel-1 and the normalized difference vegetation index (NDVI) from Sentinel-2, for detecting the mowing events based on long short-term memory (LSTM). With integrating our noise reduction step to the LSTM neural network architecture, we improved the F_1 -score from 0.69 to 0.76.