A novel spectral-temporal Bayesian unmixing algorithm with spatial prior for Sentinel-2 time series

Abstract

ABSTRACT Temporal unmixing of Sentinel-2 time series is very challenging due to the heterogeneity in spatial, spectral and temporal domains. To tackle this problem, we develop an advanced spectral-temporal Bayesian unmixing approach, with the following characteristics. First, a heterogenous noise model is designed to address the noise variation across spectral bands and time frames. Second, a conditional distribution of endmembers is designed to better characterize endmembers in heterogeneous mixed pixels. Third, the spatial prior is used to better exploit the spatial information for enhanced abundance estimates and noise resistance. Last, the proposed Bayesian framework is solved by a block coordinate descent strategy to better estimate endmembers and abundances. Experiments using both synthetic and real Sentinel-2 time series demonstrate that the proposed approach provides improved unmixing result compared with existing methods.