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Proceedings Paper

Comparison of very high spatial resolution satellite image segmentations
Author(s): Alexandre P. Carleer; Olivier Debeir; Eleonore Wolff
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Paper Abstract

Since 1999, very high spatial resolution data represent the surface of the earth with more details. However, information extraction by computer-assisted classification techniques proves to be very complex owing to the internal variability increase in land-cover units and to the weakness of spectral resolution. The increase in variability decreases the statistical separability of land-cover classes in the spectral space. Per pixel multispectral classification techniques are then insufficient for an extraction of complex categories and spectrally heterogeneous land-cover, like urban areas. Per region classification was proposed as an alternative approach. The first step of this approach is the segmentation. A large variety of segmentation algorithms were developed these last 20 years and a comparison of their implementation on very high spatial resolution images is necessary. For this study, four algorithms from the two main groups of segmentation algorithms (boundary-based and region-based algorithms) were selected. In order to compare the algorithms, an evaluation of each algorithm was carried out with empirical discrepancy evaluation methods. This evaluation is carried out with a visual segmentation of IKONOS panchromatic images.

Paper Details

Date Published: 5 February 2004
PDF: 11 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.511027
Show Author Affiliations
Alexandre P. Carleer, Univ. Libre de Bruxelles (Belgium)
Olivier Debeir, Univ. Libre de Bruxelles (Belgium)
Eleonore Wolff, Univ. Libre de Bruxelles (Belgium)

Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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