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20 December 2024 Image resolution enhancing of Sentinel-2 red edge bands via Pléiades-1 multispectral data and fast convex deep learning
Chia-Hsiang Lin, Si-Sheng Young, Cynthia Liu, Li-Yu Chang, Tun-Yu Liao
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Proceedings Volume 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII; 132660U (2024) https://doi.org/10.1117/12.3042609
Event: Asia-Pacific Remote Sensing, 2024, Kaohsiung, Taiwan
Abstract
To enhance the spatial resolution of VRE bands of Sentinel-2 image and perform a jointed analysis with Pléiades-1 images, the renowned small-data learning theory, convex/deep (CODE), is employed, forming a completely unsupervised high-quality synthesis system with fast closed-form implementation. More specifically, we formulate this synthesis problem as a satellite image fusion problem (i.e., Sentinel-2 and Pléiades-1 images). Initially, both images are fused by the proposed unsupervised Deep Residual Convolution Network (DRCN). The remarkable AI property, Deep Image Prior (DIP), enables DRCN to generate a preliminary rough solution entailing feasible spectral characteristics. Subsequently the convex Q-quadratic norm is leveraged to bridge the convex optimization and deep learning. Eventually, a powerful convex solver, the Alternating Direction Method of Multipliers (ADMM), is employed for solving the convex problem; this is the essence of CODE theory. Owing to the superiority of small-data learning technology, the high-quality synthesis of 4m/2.5m image products in multiple VRE bands for Sentinel-2 data can be acquired in a completely unsupervised manner.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chia-Hsiang Lin, Si-Sheng Young, Cynthia Liu, Li-Yu Chang, and Tun-Yu Liao "Image resolution enhancing of Sentinel-2 red edge bands via Pléiades-1 multispectral data and fast convex deep learning", Proc. SPIE 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII, 132660U (20 December 2024); https://doi.org/10.1117/12.3042609
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KEYWORDS
Image fusion
Deep learning
Image enhancement
Image resolution
Resolution enhancement technologies
Vegetation
Spatial resolution
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