Comparison of very high spatial resolution satellite image segmentations

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 variabilitv increase in land-cover units and to the weakness of spectral resolution(1,2,3). The increase in variabilitv decreases the statistical separability of land-cover classes in the spectral space(4). Per pixel multispectral classification techniques are then insufficient for an extraction of complex categories and spectrally heterogeneous land-cover, like urban areas(5). Per region classification was proposed as an alternative approach(6,7). The first step of this approach is the segmentation. A large variety of segmentation algorithms were developed these last 20 years(8) 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.