Meris Based Land Cover Classification with Self-organizing Maps: Preliminary Results

With the recent launch of MERIS, a wide range of new possibilities for the periodic land cover characterization at regional scale is available. This sensor offers a combination of innovative features, such as high spectral and temporal resolutions, wide geographical coverage and improved atmospheric correction. We believe that the exploitation of data obtained by this new sensor fills previous technological gaps, improving automatic land cover classes’ discrimination. At the same time, the extra spectral information provided by MERIS can introduce some difficulties on land cover characterization with long-established classification techniques, e.g. k-Nearest Neighbour. In this paper we report the performance of artificial neural networks (ANNs) in the context of high spectral dimensional satellite image classification. The main goal of this research is to assess the potential of the Self-Organizing Maps (SOM) neural network to extract complex land cover type information from medium resolution satellite imagery. The study was carried out with MERIS Full Resolution data from 2004 for the continental Portuguese territory.