An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures
| Autor: | Chia-Hsiang Lin, Chong-Yung Chi, Yao-Rong Syu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Endmember
Hyperspectral imaging General Computer Science Computer science outlier effects General Engineering endmember variability block successive upper bound minimization (BSUM) block coordinate descent (BCD) method Outlier Curve fitting General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering Electrical and Electronic Engineering Cluster analysis alternating direction method of multipliers (ADMM) lcsh:TK1-9971 Algorithm Block (data storage) |
| Zdroj: | IEEE Access, Vol 7, Pp 15086-15101 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2018.2890278 |
| Popis: | Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials' signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene. Recently, investigating HU under the non-negligible endmember variability (EV) and outlier effects (OE) has drawn extensive attention. Some state-of-the-art works either consider EV or consider OE, but none of them considers both EV and OE simultaneously. In this paper, we propose a novel HU algorithm, referred to as the variability/outlier-insensitive multi-convex unmixing (VOIMU) algorithm, which is robust against both EV and OE. Considering two suitable regularizers, a nonconvex minimization problem is formulated for which the perturbed linear mixing model proposed by Thouvenin et al., is used for modeling EV, while OE is implicitly handled by applying a p quasi-norm to the data fitting with 0 |
| Databáze: | OpenAIRE |
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