  Multitemporal hyperspectral image unmixing (MTHU) holds significant
importance in monitoring and analyzing the dynamic changes of surface. However,
compared to single-temporal unmixing, the multitemporal approach demands
comprehensive consideration of information across different phases, rendering
it a greater challenge. To address this challenge, we propose the Multitemporal
Hyperspectral Image Unmixing Transformer (MUFormer), an end-to-end unsupervised
deep learning model. To effectively perform multitemporal hyperspectral image
unmixing, we introduce two key modules: the Global Awareness Module (GAM) and
the Change Enhancement Module (CEM). The Global Awareness Module computes
self-attention across all phases, facilitating global weight allocation. On the
other hand, the Change Enhancement Module dynamically learns local temporal
changes by comparing endmember changes between adjacent phases. The synergy
between these modules allows for capturing semantic information regarding
endmember and abundance changes, thereby enhancing the effectiveness of
multitemporal hyperspectral image unmixing. We conducted experiments on one
real dataset and two synthetic datasets, demonstrating that our model
significantly enhances the effect of multitemporal hyperspectral image
unmixing.
