ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 237–244, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-237-2022
 
17 May 2022
17 May 2022

SEMANTIC SEGMENTATION OF HISTORICAL PHOTOGRAPHS OF THE ANTARCTICA PENINSULA

F. Dahle1, J. Tanke2, B. Wouters1,3, and R. Lindenbergh1 F. Dahle et al.
  • 1Dept. of Geoscience & Remote Sensing, Delft University of Technology, the Netherlands
  • 2Dept. of Information Systems and Artificial Intelligence, University of Bonn, Germany
  • 3Institute for Marine and Atmospheric Research Utrecht, Department of Physics, Utrecht University, Utrecht, the Netherlands

Keywords: Semantic Segmentation, Historic images, Cryospheric Images, U-Net, Machine Learning, Antarctica

Abstract. A huge archive of historical images of the Antarctica taken by the US Navy between 1940 and 2000 is publicly available. These images have not yet been used for large-scale computer-driven analysis as they were captured with analog cameras. They were only later digitized and contain no semantic information. Most modern deep-learning based semantic segmentation algorithms are trained on modern images and fail on these scanned historical images, due to varying image quality, lack of color information, and most crucially, due to artifacts in both imaging as well as scanning (e.g. Newton’s rings). The analysis of such historic data can give a view on Antarctica’s glaciers predating modern satellite imagery and provide a unique insight into the long-term impact of changing climate conditions with essential validation data for climate modelling. An important first step for analysis of such data is the extraction and localization of semantic information, e.g. where in the image is water, rocks, or snow. In this work we present the first deep-learning based method to perform semantic segmentation on historical imagery archives of the Antarctic Peninsula. Our results show that our method can handle very challenging images even after being trained with only a low number of training data and catch the general semantic meaning of a scene. For eight test images we achieve an accuracy of 74%, where the majority of errors can be explained by the classification of ice as snow.