Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image
Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, strip...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2141659 |
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| author | Payam Sajadi Mehdi Gholamnia Stefania Bonafoni Francesco Pilla |
| author_facet | Payam Sajadi Mehdi Gholamnia Stefania Bonafoni Francesco Pilla |
| author_sort | Payam Sajadi |
| collection | DOAJ |
| description | Hyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R2, RMSE, rRMSE and skewness in most bands [Formula: see text]). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed. |
| format | Article |
| id | doaj-art-a68594113b1c4e09811554bb16981302 |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-a68594113b1c4e09811554bb169813022025-08-20T02:38:11ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155162264310.1080/22797254.2022.2141659Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral imagePayam Sajadi0Mehdi Gholamnia1Stefania Bonafoni2Francesco Pilla3Spatial Dynamics Lab, School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin 4, D04 V1W8, IrelandDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, 6617715175, IranDepartment of Engineering, University of Perugia, Perugia, ItalySpatial Dynamics Lab, School of Architecture, Planning and Environmental Policy, University College Dublin, Dublin 4, D04 V1W8, IrelandHyperspectral imageries are often degraded by systematic sensor-based errors known as “striping noises”. This study implements a spectral-based regression algorithm from highly correlated consecutive bands, i.e. left band, right band or both, to model and reconstruct the abnormal pixel values, stripe noises, in various bands of PRISMA (PRecursore IperSpettrale della Missione Applicativa) imagery. The modeling performance was evaluated based on the statistical difference between the reconstructed images’ pixel values (reflectance) and their corresponding original pixel values. Results referred to the model’s high accuracy in R2, RMSE, rRMSE and skewness in most bands [Formula: see text]). Furthermore, the results indicated that the combination of both bands had higher accuracy and pixels’ homogeneity preservation compared to single-band modeling. Our findings suggested that the algorithm significantly depends on the spectral similarities between neighboring bands so that the higher spectral similarities lead to the higher model performance and vice versa. Subsequently, the minimum model performance was observed in band 143 due to lower spectral similarity, lower spectral correlation and higher wavelength differences with its adjacent right band. Finally, the study suggests that alongside other methods, our algorithm may be used as a reliable, straightforward and accurate alternative for destriping different Earth observation satellite imageries. Limitations of the proposed approach are also discussed.https://www.tandfonline.com/doi/10.1080/22797254.2022.2141659Destripinghyperspectral imagePRISMAspectral-based regressionstripe noise |
| spellingShingle | Payam Sajadi Mehdi Gholamnia Stefania Bonafoni Francesco Pilla Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image European Journal of Remote Sensing Destriping hyperspectral image PRISMA spectral-based regression stripe noise |
| title | Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image |
| title_full | Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image |
| title_fullStr | Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image |
| title_full_unstemmed | Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image |
| title_short | Spectral-based regression model for destriping of abnormal pixel values in PRISMA hyperspectral image |
| title_sort | spectral based regression model for destriping of abnormal pixel values in prisma hyperspectral image |
| topic | Destriping hyperspectral image PRISMA spectral-based regression stripe noise |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2141659 |
| work_keys_str_mv | AT payamsajadi spectralbasedregressionmodelfordestripingofabnormalpixelvaluesinprismahyperspectralimage AT mehdigholamnia spectralbasedregressionmodelfordestripingofabnormalpixelvaluesinprismahyperspectralimage AT stefaniabonafoni spectralbasedregressionmodelfordestripingofabnormalpixelvaluesinprismahyperspectralimage AT francescopilla spectralbasedregressionmodelfordestripingofabnormalpixelvaluesinprismahyperspectralimage |