Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images
Hyperspectral images contain rich spatial distribution and spectral information of land features, but they also introduce high information redundancy and computational complexity. This paper proposes dimensionality reduction methods that integrate spatial-spectral preservation and minimum noise frac...
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EDP Sciences
2025-01-01
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| Schriftenreihe: | Journal of the European Optical Society-Rapid Publications |
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| _version_ | 1839624729500057600 |
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| author | Zhou Bing Deng Lei Ying Jiaju Wang Qianghui Cheng Yue |
| author_facet | Zhou Bing Deng Lei Ying Jiaju Wang Qianghui Cheng Yue |
| author_sort | Zhou Bing |
| collection | DOAJ |
| description | Hyperspectral images contain rich spatial distribution and spectral information of land features, but they also introduce high information redundancy and computational complexity. This paper proposes dimensionality reduction methods that integrate spatial-spectral preservation and minimum noise fraction (MNF) to better analyze and utilize the spatial and spectral information in hyperspectral images. While performing the minimum noise separation transformation, the proposed method aims to preserve the spatial structure of the image as much as possible, maximizing both the signal-to-noise ratio and the spatial structure similarity of the image. The component selection strategy involves grouping components and calculating the average change in the relative position of all pixels in the feature space. The component group that most closely matches the spectral relative position before transformation is selected as the final dimensionality reduction result. Experimental results demonstrate that the proposed method is highly sensitive to noise estimation and requires a relatively accurate noise covariance matrix. The method effectively preserves spatial information, with negligible impact on the accuracy of object detection methods, and outperforms other comparative approaches. It ensures the effectiveness of downstream object detection tasks while significantly reducing computational time. The code of the proposed method is available at https://github.com/aosilu/spatial-spectral-preservation-MNF. |
| format | Article |
| id | doaj-art-e7965dae4b2b4e90b9398cc7e2fe6597 |
| institution | Matheson Library |
| issn | 1990-2573 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Journal of the European Optical Society-Rapid Publications |
| spelling | doaj-art-e7965dae4b2b4e90b9398cc7e2fe65972025-07-18T08:22:42ZengEDP SciencesJournal of the European Optical Society-Rapid Publications1990-25732025-01-012123110.1051/jeos/2025029jeos20250029Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral imagesZhou Bing0Deng Lei1Ying Jiaju2Wang Qianghui3Cheng Yue4Shijiazhuang Campus, Army Engineering University of PLAShijiazhuang Campus, Army Engineering University of PLAShijiazhuang Campus, Army Engineering University of PLAState Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information SystemShijiazhuang Campus, Army Engineering University of PLAHyperspectral images contain rich spatial distribution and spectral information of land features, but they also introduce high information redundancy and computational complexity. This paper proposes dimensionality reduction methods that integrate spatial-spectral preservation and minimum noise fraction (MNF) to better analyze and utilize the spatial and spectral information in hyperspectral images. While performing the minimum noise separation transformation, the proposed method aims to preserve the spatial structure of the image as much as possible, maximizing both the signal-to-noise ratio and the spatial structure similarity of the image. The component selection strategy involves grouping components and calculating the average change in the relative position of all pixels in the feature space. The component group that most closely matches the spectral relative position before transformation is selected as the final dimensionality reduction result. Experimental results demonstrate that the proposed method is highly sensitive to noise estimation and requires a relatively accurate noise covariance matrix. The method effectively preserves spatial information, with negligible impact on the accuracy of object detection methods, and outperforms other comparative approaches. It ensures the effectiveness of downstream object detection tasks while significantly reducing computational time. The code of the proposed method is available at https://github.com/aosilu/spatial-spectral-preservation-MNF.https://jeos.edpsciences.org/articles/jeos/full_html/2025/02/jeos20250029/jeos20250029.htmlhyperspectral imagedimensionality reductionminimum noise fraction (mnf)spatial-spectral preservation |
| spellingShingle | Zhou Bing Deng Lei Ying Jiaju Wang Qianghui Cheng Yue Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images Journal of the European Optical Society-Rapid Publications hyperspectral image dimensionality reduction minimum noise fraction (mnf) spatial-spectral preservation |
| title | Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images |
| title_full | Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images |
| title_fullStr | Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images |
| title_full_unstemmed | Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images |
| title_short | Dimensionality reduction method based on spatial-spectral preservation and minimum noise fraction for hyperspectral images |
| title_sort | dimensionality reduction method based on spatial spectral preservation and minimum noise fraction for hyperspectral images |
| topic | hyperspectral image dimensionality reduction minimum noise fraction (mnf) spatial-spectral preservation |
| url | https://jeos.edpsciences.org/articles/jeos/full_html/2025/02/jeos20250029/jeos20250029.html |
| work_keys_str_mv | AT zhoubing dimensionalityreductionmethodbasedonspatialspectralpreservationandminimumnoisefractionforhyperspectralimages AT denglei dimensionalityreductionmethodbasedonspatialspectralpreservationandminimumnoisefractionforhyperspectralimages AT yingjiaju dimensionalityreductionmethodbasedonspatialspectralpreservationandminimumnoisefractionforhyperspectralimages AT wangqianghui dimensionalityreductionmethodbasedonspatialspectralpreservationandminimumnoisefractionforhyperspectralimages AT chengyue dimensionalityreductionmethodbasedonspatialspectralpreservationandminimumnoisefractionforhyperspectralimages |