An artificial neural networks approach to map land use/cover using Landsat imagery and ancillary data

Presents a procedure for mapping land use/cover combining the spectral information from a recent image and data about spatial distribution of land use/cover types obtained from outdated cartography and ancillary data. Two fuzzy maps, which indicate the membership of each land use/cover class, were generated from the ancillary and spectral data, respectively, using an artificial neural networks approach. The combination of both maps was obtained using fuzzy rules. In comparison with spectral classification, this procedure allowed improving the accuracy of land use/cover classification from 67% to 79%.