  Multitemporal spectral unmixing (SU) is a powerful tool to process
hyperspectral image (HI) sequences due to its ability to reveal the evolution
of materials over time and space in a scene. However, significant spectral
variability is often observed between collection of images due to variations in
acquisition or seasonal conditions. This characteristic has to be considered in
the design of SU algorithms. Because of its good performance, the multiple
endmember spectral mixture analysis algorithm (MESMA) has been recently used to
perform SU in multitemporal scenarios arising in several practical
applications. However, MESMA does not consider the relationship between the
different HIs, and its computational complexity is extremely high for large
spectral libraries. In this work, we propose an efficient multitemporal SU
method that exploits the high temporal correlation between the abundances to
provide more accurate results at a lower computational complexity. We propose
to solve the complex general multitemporal SU problem by separately addressing
the endmember selection and the abundance estimation problems. This leads to a
simpler solution without sacrificing the accuracy of the results. We also
propose a strategy to detect and address abrupt abundance variations.
Theoretical results demonstrate how the proposed method compares to MESMA in
terms of quality, and how effective it is in detecting abundance changes. This
analysis provides valuable insight into the conditions under which the
algorithm succeeds. Simulation results show that the proposed method achieves
state-of-the-art performance at a smaller computational cost.
