Multiscale Representation of Brownfield Sites with Ikonos Imagery

Currently, remotely sensed data for the study of regional and global scale environmental change are available from a multitude of sensors. Each has its own intrinsic characteristics and so the choice becomes more challenging since these characteristics determine the suitability of the dataset for any particular task. Once a dataset has been identified, the method of extracting the information on land cover and its transformations must be considered, because the accuracy of the final data processed may depend on the method used. In remote sensing techniques the informational classes of a thematic mapping are not directly registered, but must be derived indirectly by using evidence contained in the spectral data of an image. Many approaches are available. Commonly, techniques that include a range of statistical, structural and neural approaches are used. Integrated strategies of classification are particularly useful, especially when information on land cover transformation is necessary in order to evaluate the effects of these processes and to provide one of the information layers needed for designing national environmental strategies. When standard procedures of per-pixel multispectral classification are applied to VHR data, the increase of spatial resolution leads to augmentation in ambiguity in the statistical definition of land cover classes and a decrease in accuracy in automatic identification. These imagery sources are likely to generate other problems. Even if the radiometric resolution is enhanced (11 bits for IKONOS or QuickBird imagery), spectral capabilities are generally limited compared to those of the previous generation sensors (seven bands for Landsat TM). Moreover, together with an increase in spatial resolution there is, usually, an increase in variability within land parcels ('noise' in the image), generating a decrease in accuracy of land use classification on a per-pixel basis. In order to solve such problems, some post-classification procedures were investigated on the basis of intrinsic contextual data information. Although a reduction of noise in the classified image was obtained, substantial improvements in overall accuracy was not seen. Moreover, a loss of meaningful information in classified data was noticed because of geometric and dimensional non-correspondence of real elements with moving window implementation matrix (for example with majority logical filter). Such methods need extensive editing operations on classified images in order to be stored in GIS databases. An alternative technique to per-pixel classification is the per-field classification (so called because fields, as opposed to pixels, are classified as independent units), which takes into account spectral and spatial …