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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2406.10724 |
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| _version_ | 1866916288768507904 |
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| author | Vyse, Ian Dagli, Rishit Chadha, Dav Vrat Ma, John P. Chen, Hector Ruparelia, Isha Seran, Prithvi Xie, Matthew Aamer, Eesa Armstrong, Aidan Black, Naveen Borstein, Ben Caldwell, Kevin Dahanaggamaarachchi, Orrin Dai, Joe Fatima, Abeer Lu, Stephanie Michet, Maxime Paul, Anoushka Po, Carrie Ann Prakash, Shivesh Prosser, Noa Roy, Riddhiman Shinjo, Mirai Shofman, Iliya Silayan, Coby Sox-Harris, Reid Zheng, Shuhan Nguyen, Khang |
| author_facet | Vyse, Ian Dagli, Rishit Chadha, Dav Vrat Ma, John P. Chen, Hector Ruparelia, Isha Seran, Prithvi Xie, Matthew Aamer, Eesa Armstrong, Aidan Black, Naveen Borstein, Ben Caldwell, Kevin Dahanaggamaarachchi, Orrin Dai, Joe Fatima, Abeer Lu, Stephanie Michet, Maxime Paul, Anoushka Po, Carrie Ann Prakash, Shivesh Prosser, Noa Roy, Riddhiman Shinjo, Mirai Shofman, Iliya Silayan, Coby Sox-Harris, Reid Zheng, Shuhan Nguyen, Khang |
| contents | Satellite remote sensing missions have gained popularity over the past fifteen years due to their ability to cover large swaths of land at regular intervals, making them ideal for monitoring environmental trends. The FINCH mission, a 3U+ CubeSat equipped with a hyperspectral camera, aims to monitor crop residue cover in agricultural fields. Although hyperspectral imaging captures both spectral and spatial information, it is prone to various types of noise, including random noise, stripe noise, and dead pixels. Effective denoising of these images is crucial for downstream scientific tasks. Traditional methods, including hand-crafted techniques encoding strong priors, learned 2D image denoising methods applied across different hyperspectral bands, or diffusion generative models applied independently on bands, often struggle with varying noise strengths across spectral bands, leading to significant spectral distortion. This paper presents a novel approach to hyperspectral image denoising using latent diffusion models that integrate spatial and spectral information. We particularly do so by building a 3D diffusion model and presenting a 3-stage training approach on real and synthetically crafted datasets. The proposed method preserves image structure while reducing noise. Evaluations on both popular hyperspectral denoising datasets and synthetically crafted datasets for the FINCH mission demonstrate the effectiveness of this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH Spacecraft Vyse, Ian Dagli, Rishit Chadha, Dav Vrat Ma, John P. Chen, Hector Ruparelia, Isha Seran, Prithvi Xie, Matthew Aamer, Eesa Armstrong, Aidan Black, Naveen Borstein, Ben Caldwell, Kevin Dahanaggamaarachchi, Orrin Dai, Joe Fatima, Abeer Lu, Stephanie Michet, Maxime Paul, Anoushka Po, Carrie Ann Prakash, Shivesh Prosser, Noa Roy, Riddhiman Shinjo, Mirai Shofman, Iliya Silayan, Coby Sox-Harris, Reid Zheng, Shuhan Nguyen, Khang Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Satellite remote sensing missions have gained popularity over the past fifteen years due to their ability to cover large swaths of land at regular intervals, making them ideal for monitoring environmental trends. The FINCH mission, a 3U+ CubeSat equipped with a hyperspectral camera, aims to monitor crop residue cover in agricultural fields. Although hyperspectral imaging captures both spectral and spatial information, it is prone to various types of noise, including random noise, stripe noise, and dead pixels. Effective denoising of these images is crucial for downstream scientific tasks. Traditional methods, including hand-crafted techniques encoding strong priors, learned 2D image denoising methods applied across different hyperspectral bands, or diffusion generative models applied independently on bands, often struggle with varying noise strengths across spectral bands, leading to significant spectral distortion. This paper presents a novel approach to hyperspectral image denoising using latent diffusion models that integrate spatial and spectral information. We particularly do so by building a 3D diffusion model and presenting a 3-stage training approach on real and synthetically crafted datasets. The proposed method preserves image structure while reducing noise. Evaluations on both popular hyperspectral denoising datasets and synthetically crafted datasets for the FINCH mission demonstrate the effectiveness of this approach. |
| title | Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH Spacecraft |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.10724 |