Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 299-303, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-299-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
21 Jun 2016
AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY
S. Mader and G. J. Grenzdörffer Rostock University, Chair for Geodesy and Geoinformatics, J.-v.-Liebig Weg 6, Rostock, Germany
Keywords: Marine birds, Aerial survey, Remote sensing, Image analysis, Graph cuts Abstract. Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.
Conference paper (PDF, 1614 KB)


Citation: Mader, S. and Grenzdörffer, G. J.: AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 299-303, https://doi.org/10.5194/isprs-archives-XLI-B7-299-2016, 2016.

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