Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH Spacecraft

• URL: http://arxiv.org/abs/2406.10724v1
• Date: Sat, 15 Jun 2024 19:34:18 GMT
• Title: Beyond the Visible: Jointly Attending to Spectral and Spatial Dimensions with HSI-Diffusion for the FINCH Spacecraft
• Authors: Ian Vyse, Rishit Dagli, Dav Vrat Chadha, John P. Ma, Hector Chen, Isha Ruparelia, Prithvi Seran, Matthew Xie, Eesa Aamer, Aidan Armstrong, Naveen Black, Ben Borstein, Kevin Caldwell, Orrin Dahanaggamaarachchi, Joe Dai, Abeer Fatima, Stephanie Lu, Maxime Michet, Anoushka Paul, Carrie Ann Po, Shivesh Prakash, Noa Prosser, Riddhiman Roy, Mirai Shinjo, Iliya Shofman, Coby Silayan, Reid Sox-Harris, Shuhan Zheng, Khang Nguyen,
• Abstract summary: The FINCH mission aims to monitor crop residue cover in agricultural fields. Hyperspectral imaging captures both spectral and spatial information. It is prone to various types of noise, including random noise, stripe noise, and dead pixels.
• Score: 2.5057561650768814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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