A SEMI-SUPERVISED APPROACH TO SAR-OPTICAL IMAGE MATCHING

Hughes, L. H.; Schmitt, M.

Matching synthetic aperture radar (SAR) and optical remote sensing imagery is a key first step towards exploiting the complementary nature of these data in data fusion frameworks. While numerous signal-based approaches to matching have been proposed, they often fail to perform well in multi-sensor situations. In recent years deep learning has become the go-to approach for solving image matching in computer vision applications, and has also been adapted to the case of SAR-optical image matching. However, the hitherto proposed techniques still fail to match SAR and optical imagery in a generalizable manner. These limitations are largely due to the complexities in creating large-scale datasets of corresponding SAR and optical image patches. In this paper we frame the matching problem within semi-supervised learning, and use this as a proxy for investigating the effects of data scarcity on matching. In doing so we make an initial contribution towards the use of semi-supervised learning for matching SAR and optical imagery. We further gain insight into the non-complementary nature of commonly used supervised and unsupervised loss functions, as well as dataset size requirements for semi-supervised matching.

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Hughes, L. H. / Schmitt, M.: A SEMI-SUPERVISED APPROACH TO SAR-OPTICAL IMAGE MATCHING. 2019. Copernicus Publications.

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Rechteinhaber: L. H. Hughes

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