The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 93–98, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-93-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 93–98, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-93-2022
 
30 May 2022
30 May 2022

CNN BASED VEHICLE TRACK DETECTION IN COHERENT SAR IMAGERY: AN ANALYSIS OF DATA AUGMENTATION

S. Kuny1, H. Hammer1, and A. Thiele1,2 S. Kuny et al.
  • 1Fraunhofer IOSB, Institute of Optronics, System Technologies, and Image Exploitation, Ettlingen, Germany
  • 2Institute of Photogrammetry and Remote Sensing IPF, Karlsruhe Institute of Technology KIT, Germany

Keywords: coherent change detection, vehicle tracks, CNN, data augmentation

Abstract. The coherence image as a product of a coherent SAR image pair can expose even subtle changes in the surface of a scene, such as vehicle tracks. For machine learning models, the large amount of required training data often is a crucial issue. A general solution for this is data augmentation. Standard techniques, however, were predominantly developed for optical imagery, thus do not account for SAR specific characteristics and thus are only partially applicable to SAR imagery. In this paper several data augmentation techniques are investigated for their performance impact regarding a CNN based vehicle track detection with the aim of generating an optimized data set. Quantitative results are shown on the performance comparison. Furthermore, the performance of the fully-augmented data set is put into relation to the training with a large non-augmented data set.