Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor

Abstract

Endmember (EM) variability has an important impact on the performance of hyperspectral image (HI) analysis algorithms. Recently, extended linear mixing models have been proposed to account for EM variability in the spectral unmixing (SU) problem. The direct use of these models has led to severely ill-posed optimization problems. Different regularization strategies have been considered to deal with this issue, but none so far has consistently exploited the information provided by the existence of multiple pure pixels often present in HIs. In this work, we propose to break the SU problem into a sequence of two problems. First, we use pure pixel information to estimate an interpolated tensor of scaling factors representing spectral variability. This is done by considering the spectral variability to be a smooth function over the HI and confining the energy of the scaling tensor to a low-rank structure. Afterwards, we solve a matrix-factorization problem to estimate the fractional abundances using the variability scaling factors estimated in the previous step, what leads to a significantly more well-posed problem. Simulation swith synthetic and real data attest the effectiveness of the proposed strategy.

References in corpus (8)

• Tensor Decomposition for Signal Processing and Machine Learning
• Nonlinear unmixing of hyperspectral images: models and algorithms
• 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
• Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images
• Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
• A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing

Cited by in corpus (4)

• Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
• Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data