An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures
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...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8600733/ |
| _version_ | 1831644379627913216 |
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| author | Yao-Rong Syu Chia-Hsiang Lin Chong-Yung Chi |
| author_facet | Yao-Rong Syu Chia-Hsiang Lin Chong-Yung Chi |
| author_sort | Yao-Rong Syu |
| collection | DOAJ |
| description | 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 <; p <; 1 . Then, we reformulate it into a multi-convex problem which is then solved by the block coordinate descent method, with convergence guarantee by casting it into the block successive upper bound minimization framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm. |
| first_indexed | 2024-12-19T13:17:05Z |
| format | Article |
| id | doaj.art-7d06c9fa594f4aaf967856428c36836e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:17:05Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d06c9fa594f4aaf967856428c36836e2022-12-21T20:19:48ZengIEEEIEEE Access2169-35362019-01-017150861510110.1109/ACCESS.2018.28902788600733An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral SignaturesYao-Rong Syu0https://orcid.org/0000-0002-8396-9241Chia-Hsiang Lin1Chong-Yung Chi2Department of Electrical Engineering, Institute of Communications Engineering, National Tsing Hua University, Hsinchu, TaiwanDepartment of Electrical Engineering (Institute of Computer and Communication Engineering), National Cheng Kung University, Tainan, TaiwanDepartment of Electrical Engineering, Institute of Communications Engineering, National Tsing Hua University, Hsinchu, TaiwanEffective 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 <; p <; 1 . Then, we reformulate it into a multi-convex problem which is then solved by the block coordinate descent method, with convergence guarantee by casting it into the block successive upper bound minimization framework. The proposed VOIMU algorithm can yield a stationary-point solution with convergence guarantee, together with some intriguing information of potential outlier pixels though outliers are neither physically modeled in the above problem nor detected in the algorithm operation. Finally, we provide some simulation results and experimental results using real data to demonstrate the efficacy and practical applicability of the proposed VOIMU algorithm.https://ieeexplore.ieee.org/document/8600733/Hyperspectral imagingendmember variabilityoutlier effectsblock successive upper bound minimization (BSUM)block coordinate descent (BCD) methodalternating direction method of multipliers (ADMM) |
| spellingShingle | Yao-Rong Syu Chia-Hsiang Lin Chong-Yung Chi An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures IEEE Access Hyperspectral imaging endmember variability outlier effects block successive upper bound minimization (BSUM) block coordinate descent (BCD) method alternating direction method of multipliers (ADMM) |
| title | An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures |
| title_full | An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures |
| title_fullStr | An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures |
| title_full_unstemmed | An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures |
| title_short | An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures |
| title_sort | outlier insensitive unmixing algorithm with spatially varying hyperspectral signatures |
| topic | Hyperspectral imaging endmember variability outlier effects block successive upper bound minimization (BSUM) block coordinate descent (BCD) method alternating direction method of multipliers (ADMM) |
| url | https://ieeexplore.ieee.org/document/8600733/ |
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