Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images

Matthias Vahl, Uwe von Lukas, Bodo Urban, Arjan Kuijper

2015

Abstract

Geo-referenced aerial images are available in very high resolution. The automated production and updating of electronic nautical charts (ENC), as well as other products (e.g. thematic maps), from aerial images is a current challenge for hydrographic organizations. Often standard vision algorithms are not reliable enough for robust object detection in natural images. We thus propose a procedure that combines processing steps on three levels, from pixel (low-level) via segments (mid-level) to semantic information (high level). We combine simple linear iterative clustering (SLIC) as an efficient low-level algorithm with a classification based on texture features by supported vector machine (SVM) and a generalized Hough transformation (GHT) for detecting shapes on mid-level. Finally, we show how semantic information can be used to improve results from the earlier processing steps in the high-level step. As standard vision methods are typically much too slow for such huge-sized images and additionally geographical references must be maintained over the complete procedure, we present a solution to overcome these problems.

Paper Citation

in Harvard Style

Vahl M., von Lukas U., Urban B. and Kuijper A. (2015). Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 100-107. DOI: 10.5220/0005301501000107

in Bibtex Style

@conference{visapp15,
author={Matthias Vahl and Uwe von Lukas and Bodo Urban and Arjan Kuijper},
title={Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={100-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005301501000107},
isbn={978-989-758-091-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - Semi-automatic Analysis of Huge Digital Nautical Charts of Coastal Aerial Images
SN - 978-989-758-091-8
AU - Vahl M.
AU - von Lukas U.
AU - Urban B.
AU - Kuijper A.
PY - 2015
SP - 100
EP - 107
DO - 10.5220/0005301501000107