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.

摘要

由于空间、光谱和时间域的异质性，Sentinel-2 时间序列的时间分解非常具有挑战性。为了解决这个问题，我们开发了一种先进的光谱-时间贝叶斯分解方法，具有以下特征。首先，设计了一个异构噪声模型来解决跨光谱带和时间范围内的噪声变化。其次，端元的条件分布旨在更好地表征异构混合像素中的端元。第三，空间先验用于更好地利用空间信息来增强丰度估计和抗噪性。最后，提出的贝叶斯框架通过块坐标下降策略解决，以更好地估计端元和丰度。