Cargando…
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...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955137/ https://www.ncbi.nlm.nih.gov/pubmed/36832628 http://dx.doi.org/10.3390/e25020260 |
_version_ | 1784894282861117440 |
---|---|
author | Fan, Ya-Ru |
author_facet | Fan, Ya-Ru |
author_sort | Fan, Ya-Ru |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9955137 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99551372023-02-25 Robust Superpixel Segmentation for Hyperspectral-Image Restoration Fan, Ya-Ru Entropy (Basel) Article 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. MDPI 2023-01-31 /pmc/articles/PMC9955137/ /pubmed/36832628 http://dx.doi.org/10.3390/e25020260 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fan, Ya-Ru Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title | Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title_full | Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title_fullStr | Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title_full_unstemmed | Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title_short | Robust Superpixel Segmentation for Hyperspectral-Image Restoration |
title_sort | robust superpixel segmentation for hyperspectral-image restoration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955137/ https://www.ncbi.nlm.nih.gov/pubmed/36832628 http://dx.doi.org/10.3390/e25020260 |
work_keys_str_mv | AT fanyaru robustsuperpixelsegmentationforhyperspectralimagerestoration |