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The TriMetrogon Aerial (TMA) archive is an archive of historical images of Antarctica taken by the US Navy between 1940 and 2000 with analogue cameras. The analysis of such historic data can give a view of Antarctica's glaciers predating modern satellite imagery and provide unique insights into the long-term impact of changing climate conditions with essential validation data for climate modelling. However, the lack of semantic information for these images presents a challenge for large-scale computer-driven analysis. Such information can be added to the data using semantic segmentation, but traditional algorithms fail on these scanned historical grayscale images, due to varying image quality, lack of colour information and artefacts in the images. To address this, we present a deep-learning-based U-net workflow. Our approach includes creating training data by pre-processing and labelling the raw images. Furthermore, different versions of the U-net are trained to optimize its hyperparameters and augmentation methods. With the optimal hyper-parameters and augmentation methods, a final model has been trained for a use-case to segment 118 images covering Adelaide Island. We tested our approach by segmenting challenging historical images using a U-net model with just 80 training images, achieving an accuracy of 73% for 20 validation images. While no test data is available for our use case, a visual examination of the segmented images shows that our method performs effectively. The comparison of the hyper-parameters and augmentation methods provides directions for training other U-net-based models so that the presented workflow can be used to segment other archives with historical imagery. Additionally, the labelled training data and the segmented images of the test are publicly available at https://github.com/fdahle/antarctic_segmentation., Physical and Space Geodesy, Optical and Laser Remote Sensing

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., Physical and Space Geodesy, Optical and Laser Remote Sensing

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., Physical and Space Geodesy, Optical and Laser Remote Sensing