Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data

TL;DR

This paper introduces an efficient multitemporal spectral unmixing method for hyperspectral data that leverages temporal correlations to improve accuracy and reduce computational costs, also capable of detecting abrupt abundance changes.

Contribution

The paper proposes a novel multitemporal spectral unmixing approach that separates endmember selection and abundance estimation, enhancing efficiency and accuracy over existing methods like MESMA.

Findings

Achieves state-of-the-art performance in multitemporal unmixing.

Reduces computational complexity compared to MESMA.

Effectively detects abrupt abundance variations over time.

Abstract

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 in time. 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.