A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing

Abstract

Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging. In hyperspectral unmixing (HU), low-rank constraints on the abundance maps have been shown to act as a regularization which adequately accounts for the multidimensional structure of the underlying signal. However, imposing a strict low-rank constraint for the abundance maps does not seem to be adequate, as important information that may be required to represent fine scale abundance behavior may be discarded. This paper introduces a new low-rank tensor regularization that adequately captures the low-rank structure underlying the abundance maps without hindering the flexibility of the solution. Simulation results with synthetic and real data show that the the extra flexibility introduced by the proposed regularization significantly improves the unmixing results.

References in corpus (7)

• Tensor Decomposition for Signal Processing and Machine Learning
• Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
• Nonlinear unmixing of hyperspectral images: models and algorithms
• 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

Cited by in corpus (10)

• 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
• Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
• Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
• A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
• Super-Resolution Reconstruction of Electrical Impedance Tomography Images
• Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
• MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
• Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability