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Comparison of classifiers of remote-sensing data for land-use/land-cover mapping



Comparison of classifiers of remote-sensing data for land-use/land-cover mapping



Current Science 86(2): 328-335



A study was conducted to evaluate the potential of the Gaussian maximum likelihood classifier, Mahalanobis minimum-distance classifier, minimum-distance classifier, and artificial neural network (ANN) classifier in deriving information on land use/land cover (wetland, water, barren land, cropland, waterlogged areas, open forest, regenerated and burnt rangelands, and dense forest) over part of Ethiopia using various band combinations of Landsat thematic mapper data.

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