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Semi-automatic analysis of huge digital nautical charts of coastal aerial images

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

:
Volltext urn:nbn:de:0011-n-3367054 (9.5 MByte PDF)
MD5 Fingerprint: 3385d4fe31766e0b8d86c61a1d4f7750


Braz, José (Ed.); Battiato, Sebastiano (Ed.); Imai, Francisco (Ed.) ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
10th International Conference on Computer Vision Theory and Applications, VISAPP 2015. Proceedings. Vol.III : Berlin, Germany, 11 - 14 March 2015; Part of VISIGRAPP, the 10th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SciTePress, 2015
ISBN: 978-989-758-091-8
pp.100-107
International Conference on Computer Vision Theory and Applications (VISAPP) <10, 2015, Berlin>
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <10, 2015, Berlin>
English
Conference Paper, Electronic Publication
Fraunhofer IGD ()
computer vision; geographic information systems (GIS); segmentation; image analysis

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.

: http://publica.fraunhofer.de/documents/N-336705.html