Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing

Abstract

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can benefit from spatial regularization strategies. While existing spatial regularization methods improve the problem conditioning and promote piecewise smooth solutions, they lead to large nonsmooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge, and a necessity for many real world applications. In this paper, a novel multiscale spatial regularization approach for sparse unmixing is proposed. The method uses a signal-adaptive spatial multiscale decomposition based on superpixels to decompose the unmixing problem into two simpler problems, one in the approximation domain and another in the original domain. Simulation results using both synthetic and real data indicate that the proposed method can outperform state-of-the-art Total Variation-based algorithms with a computation time comparable to that of their unregularized counterparts.

References in corpus (3)

• Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
• A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
• A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing

Cited by in corpus (23)

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• Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
• Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
• Coupled Tensor Decomposition for Hyperspectral and Multispectral Image Fusion with Inter-Image Variability
• Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
• Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
• A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
• Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
• Super-Resolution Reconstruction of Electrical Impedance Tomography Images
• Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
• Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
• A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data
• Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
• Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
• Online Graph-Based Change Point Detection in Multiband Image Sequences
• SUnAA: Sparse Unmixing using Archetypal Analysis
• A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm
• Fast Semisupervised Unmixing Using Nonconvex Optimization
• Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
• Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis
• Tech Report: A Homogeneity-Based Multiscale Hyperspectral Image Representation for Sparse Spectral Unmixing