Multisource classification of complex rural areas by statistical and neural-network approaches

Description

The automatic generation of land-cover inventories by using remote-sensing data is a very difficult task when complex rural areas are involved. The main difficulties are related to the characterization of such spectrally complex and heterogeneous environments and to the choice of an effective classification approach. In this paper, the usefulness of spectral (Landsat-5 Thematic Mapper images), texture (grey-level cooccurrence matrix statistics), and ancillary (terrain elevation, slope, and aspect) data to characterize two complex rural areas in central Italy is quantitatively demonstrated. A statistical and a neural-network classification approach are applied to such a multisource data set, and their classification performances are assessed and compared. The classification performances of the two approaches are quantitatively evaluated in terms of global and conditional Kappa accuracies. The Zeta statistics is used to evaluate the statistical significance of the different classification accuracies obtained by the two approaches by using multisource data.

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