A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithm

Abstract

In hyperspectral sparse unmixing, a successful approach employs spectral bundles to address the variability of the endmembers in the spatial domain. However, the regularization penalties usually employed aggregate substantial computational complexity, and the solutions are very noise-sensitive. We generalize a multiscale spatial regularization approach to solve the unmixing problem by incorporating group sparsity-inducing mixed norms. Then, we propose a noise-robust method that can take advantage of the bundle structure to deal with endmember variability while ensuring inter- and intra-class sparsity in abundance estimation with reasonable computational cost. We also present a general heuristic to select the \emph{most representative} abundance estimation over multiple runs of the unmixing process, yielding a solution that is robust and highly reproducible. Experiments illustrate the robustness and consistency of the results when compared to related methods.

References in corpus (5)

• 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
• Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms

Cited by in corpus (1)

• Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis