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arXiv · 1704.02162

Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Manuel López-Radcenco·Ronan Fablet·Abdeldjalil Aïssa-El-Bey·Pierre Ailliot
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Abstract

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions.

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Manuel López-Radcenco, Ronan Fablet, Abdeldjalil Aïssa-El-Bey, Pierre Ailliot. 2017-04-07. Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data. https://arxiv.org/abs/1704.02162

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