Assessment of Very High Spatial Resolution Satellite Image Segmentations

By Alexandre P. Carleer, Olivier Debeir, Eléonore Wolff. 2005

305 citations recorded by the source corpus.

Since 1999, very high spatial resolution satellite data represent the surface of the Earth with more detail. However, information extraction by per pixel multispectral classification techniques proves to be very complex owing to the internal variability increase in land-cover units and to the weakness of spectral resolution. Image segmentation before classification was proposed as an alternative approach, but a large variety of segmentation algorithms were developed during the last 20 years, and a comparison of their implementation on very high spatial resolution images is necessary. In this study, four algorithms from the two main groups of segmentation algorithms (boundarybased and region-based) were evaluated and compared. 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. The results show that the choice of parameters is very important and has a great influence on the segmentation results. The selected boundary-based algorithms are sensitive to the noise or texture. Better results are obtained with regionbased algorithms, but a problem with the transition zones between the contrasted objects can be present.

Subfields: Artificial Intelligence, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/a335ee2a136bb3d445b0c27c27f3fb1f280d5a23

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Citations in the corpus

Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, methodology): “Most studies employing supervised methods used them to evaluate the quality of single-scale segmentations (Paglieroni, 2004; Carleer et al., 2005; Chabrier et al., 2006; Moller et al., 2007; Neubert et al., 2008).”
Object based image analysis for remote sensing (2010, methodology): “Similarly, Carleer et al. (2005) compared four segmentation algorithms from the twomain groups of segmentation algorithms (boundary-based and region-based), applied on very high spatial resolution images for different landscapes, and differentiated urban areas into residential, urban administrative…”