ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 25–33, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-25-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 25–33, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-25-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Aug 2020

03 Aug 2020

DEEP LEARNING BASED FEATURE MATCHING AND ITS APPLICATION IN IMAGE ORIENTATION

L. Chen, F. Rottensteiner, and C. Heipke L. Chen et al.
  • Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany

Keywords: Image Matching, Affine Shape Estimation, Descriptor Learning, Feature Orientation, Image Orientation

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.