Authors:
Matthias Vahl
;
Uwe von Lukas
;
Bodo Urban
and
Arjan Kuijper
Affiliation:
Fraunhofer IGD, Germany
Keyword(s):
Semi-automatic Analysis, Electronic Nautical Charts, Coastal Aerial Images.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Document Imaging in Business
;
Image and Video Analysis
;
Shape Representation and Matching
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.
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