Use of spatial information after segmentation for very high spatial resolution satellite data classification

Abstract

Since 1999, very high spatial resolution satellite data (IKONOS, QuickBird, OrbView_3) represent the surface of the earth with more details. However, these data don't provide necessarily better land cover/use classification. These incongruous results of earlier studies were attributed to the increase of the internal variability within the homogenous land cover unit and the weakness of spectral resolution. To overcome these problems, a region based procedure can be used. The image segmentation before the classification is successful at removing much of the structural clutter and allows an easy use of spatial information for classification. This information, on top of spectral information, can be the surface, the perimeter, the compactness (area/perimeter2), the degree and kind of texture. In this study, a feature selection method is used to show which features are useful for which classes and the use of these features to improve the land cover/use classification of very high spatial resolution satellite image. The features selection is preceded by an analysis of visual interpretation parameters useful for the identification of each class of the legend, in order to guide the choice of the features whose combinations can be numerous.