Urban land cover multi-level region-based classification of VHR data by selecting relevant features

The limited spatial resolution of satellite images used to be a problem for the adequate definition of the urban environment. This problem was expected to be solved with the availability of very high spatial resolution satellite images (IKONOS, QuickBird, OrbView-3). However, these space-borne sensors are limited to four multi-spectral bands and may have specific limitations as far as detailed urban area mapping is concerned. It is therefore essential to combine spectral information with other information, such as the features used in visual interpretation (e.g. the degree and kind of texture and the shape) transposed to digital analysis. In this study, a feature selection method is used to show which features are useful for particular land-cover classes. These features are used to improve the land-cover classification of very high spatial resolution satellite images of urban areas. The useful features are compared with a visual feature selection. The features are calculated after segmentation into regions that become analysis units and ease the feature calculation.