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

Abstract · Apr 7, 2017 09:51 ·

resolution super ssh negativity remote irregularly adapted brest stat-ml

Arxiv Abstract

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

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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