Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks

Abstract

Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adequately accounting for the spatial and temporal variability of the endmembers in MTHU is challenging, and has not been fully addressed so far in unsupervised frameworks. In this work, we propose an unsupervised MTHU algorithm based on variational recurrent neural networks. First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process. Moreover, a new model based on a low-dimensional parametrization is used to represent spatial and temporal endmember variability, significantly reducing the amount of variables to be estimated. We propose to formulate MTHU as a Bayesian inference problem. However, the solution to this problem does not have an analytical solution due to the nonlinearity and non-Gaussianity of the model. Thus, we propose a solution based on deep variational inference, in which the posterior distribution of the estimated abundances and endmembers is represented by using a combination of recurrent neural networks and a physically motivated model. The parameters of the model are learned using stochastic backpropagation. Experimental results show that the proposed method outperforms state of the art MTHU algorithms.

References in corpus (19)

• A Critical Review of Recurrent Neural Networks for Sequence Learning
• Tutorial on Variational Autoencoders
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
• Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
• Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
• Enhancing hyperspectral image unmixing with spatial correlations
• Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
• Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
• A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
• Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms
• A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
• Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability
• Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model
• Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity or Mismodelling Effects
• Dynamical spectral unmixing of multitemporal hyperspectral images
• Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
• Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability
• A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
• Illumination invariant hyperspectral image unmixing based on a digital surface model

Cited by in corpus (1)

• Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing