ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume IV-2/W7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 71–78, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 71–78, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019

  16 Sep 2019

16 Sep 2019

A SEMI-SUPERVISED APPROACH TO SAR-OPTICAL IMAGE MATCHING

L. H. Hughes and M. Schmitt L. H. Hughes and M. Schmitt
  • Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany

Keywords: Image Matching, Synthetic Aperture Radar (SAR), Optical Remote Sensing, Deep Learning, Deep Matching, Semi-supervised Learning

Abstract. 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.