Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data

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.

References in corpus (18)

• Deep learning in remote sensing: a review
• Graph Convolutional Networks for Hyperspectral Image Classification
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
• Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)
• Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
• Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
• Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
• Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
• A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
• A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
• Dynamical spectral unmixing of multitemporal hyperspectral images
• Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
• Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
• A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
• Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
• Online Graph-Based Change Point Detection in Multiband Image Sequences