Robust Superpixel Segmentation for Hyperspectral-Image Restoration

Hyperspectral-image (HSI) restoration plays an essential role in remote sensing image processing. Recently, superpixel segmentation-based the low-rank regularized methods for HSI restoration have shown outstanding performance. However, most of them simply segment the HSI according to its first princ...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2023-01, Vol.25 (2), p.260
Main Author: Fan, Ya-Ru
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Decomposition
hyperspectral-image restoration
Image processing
Image restoration
Image segmentation
Methods
Noise
Pixels
principal component
Principal components analysis
Remote sensing
Robust statistics
robust superpixel segmentation
Robustness
weighted nuclear norm
Citations: Items that this one cites
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Summary:Hyperspectral-image (HSI) restoration plays an essential role in remote sensing image processing. Recently, superpixel segmentation-based the low-rank regularized methods for HSI restoration have shown outstanding performance. However, most of them simply segment the HSI according to its first principal component, which is suboptimal. In this paper, integrating the superpixel segmentation with principal component analysis, we propose a robust superpixel segmentation strategy to better divide the HSI, which can further enhance the low-rank attribute of the HSI. To better employ the low-rank attribute, the weighted nuclear norm by three types of weighting is proposed to efficiently remove the mixed noise in degraded HSI. Experiments conducted on simulated and real HSI data verify the performance of the proposed method for HSI restoration.
ISSN:1099-4300
1099-4300
DOI:10.3390/e25020260