Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909925976834048 |
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| author | Nguyen, Thi Thuy Nga Dorffer, Clément Jourdin, Frédéric Fablet, Ronan |
| author_facet | Nguyen, Thi Thuy Nga Dorffer, Clément Jourdin, Frédéric Fablet, Ronan |
| contents | Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of interest, as well as the diversity of processes and scales involved. Considering region-specific and parameter-specific neural mapping schemes will result in substantial training costs. This study addresses generalization performance of neural mapping schemes to deliver gap-free satellite-derived ocean colour products. We develop a comprehensive experimental framework using real multi-sensor ocean colour datasets for two regions (the Mediterranean Sea and the North Sea) and a representative set of bio-optical parameters (Chlorophyll-a concentration, suspended particulate matter concentration, particulate backscattering coefficient). We consider several neural mapping schemes, and we report excellent generalization performance across regions and bio-optical parameters without any fine-tuning using appropriate dataset-specific normalization procedures. We discuss further how these results provide new insights towards the large-scale deployment of neural schemes for the processing of satellite-derived ocean colour datasets beyond case-study-specific demonstrations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_11588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets Nguyen, Thi Thuy Nga Dorffer, Clément Jourdin, Frédéric Fablet, Ronan Image and Video Processing I.2.10; I.4.5 Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of interest, as well as the diversity of processes and scales involved. Considering region-specific and parameter-specific neural mapping schemes will result in substantial training costs. This study addresses generalization performance of neural mapping schemes to deliver gap-free satellite-derived ocean colour products. We develop a comprehensive experimental framework using real multi-sensor ocean colour datasets for two regions (the Mediterranean Sea and the North Sea) and a representative set of bio-optical parameters (Chlorophyll-a concentration, suspended particulate matter concentration, particulate backscattering coefficient). We consider several neural mapping schemes, and we report excellent generalization performance across regions and bio-optical parameters without any fine-tuning using appropriate dataset-specific normalization procedures. We discuss further how these results provide new insights towards the large-scale deployment of neural schemes for the processing of satellite-derived ocean colour datasets beyond case-study-specific demonstrations. |
| title | Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets |
| topic | Image and Video Processing I.2.10; I.4.5 |
| url | https://arxiv.org/abs/2503.11588 |