Abstract
This paper deals with the evaluation of the performance of a set of classifiers on multispectral imagery with low dimensionality and low spatial and spectral resolutions. The original Landsat TM images and other 4 transformed sets are classified by 5 supervised and 2 unsupervised methods. The results for 7 land cover classes are compared and the performances of the methods for each set of input data are discussed.
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Ferreiro-Armán, M., Bandeira, L.P.C., Martín-Herrero, J., Pina, P. (2007). Classifiers for Vegetation and Forest Mapping with Low Resolution Multiespectral Imagery. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_24
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DOI: https://doi.org/10.1007/978-3-540-72847-4_24
Publisher Name: Springer, Berlin, Heidelberg
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