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Proceedings Paper

Detection of hedges based on attribute filters
Author(s): Gabrielle Cavallaro; Benoit Arbelot; Mathieu Fauvel; Mauro Dalla Mura; Jón Atli Benediktsson; Lorenzo Bruzzone; Jocelyn Chanussot; David Sheeren
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Paper Abstract

The detection of hedges is a very important task for the monitoring of a rural environment and aiding the management of their related natural resources. Hedges are narrow vegetated areas composed of shrubs and/or trees that are usually present at the boundaries of adjacent agricultural fields. In this paper, a technique for detecting hedges is presented. It exploits the spectral and spatial characteristics of hedges. In detail, spatial features are extracted with attribute filters, which are connected operators defined in the mathematical morphology framework. Attribute filters are flexible operators that can perform a simplification of a grayscale image driven by an arbitrary measure. Such a measure can be related to characteristics of regions in the scene such as the scale, shape, contrast etc. Attribute filters can be computed on tree representations of an image (such as the component tree) which either represent bright or dark regions (with respect to their surroundings graylevels). In this work, it is proposed to compute attribute filters on the inclusion tree which is an hierarchical dual representation of an image, in which nodes of the tree corresponds to both bright and dark regions. Specifically, attribute filters are employed to aid the detection of woody elements in the image, which is a step in the process aimed at detecting hedges. In order to perform a characterization of the spatial information of the hedges in the image, different attributes have been considered in the analysis. The final decision is obtained by fusing the results of different detectors applied to the filtered image.

Paper Details

Date Published: 8 November 2012
PDF: 9 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 853712 (8 November 2012); doi: 10.1117/12.999360
Show Author Affiliations
Gabrielle Cavallaro, Univ. degli Studi di Trento (Italy)
Univ. of Iceland (Iceland)
Benoit Arbelot, Univ. of Iceland (Iceland)
GIPSA-lab (France)
Mathieu Fauvel, DYNAFOR Lab., INRA (France)
Univ. of Toulouse (France)
Mauro Dalla Mura, GIPSA-Lab (France)
Jón Atli Benediktsson, The Univ. of Iceland (Iceland)
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)
Jocelyn Chanussot, GIPSA-Lab (France)
David Sheeren, DYNAFOR Lab., INRA (France)
Univ. of Toulouse (France)


Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)

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