Land use image classification through optimum-path forest clustering
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Data
2011-11-16
Autores
Pisani, R. [UNESP]
Riedel, P. [UNESP]
Ferreira, M. [UNESP]
Marques, M. [UNESP]
Mizobe, R. [UNESP]
Papa, J. [UNESP]
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Resumo
Land use classification has been paramount in the last years, since we can identify illegal land use and also to monitor deforesting areas. Although one can find several research works in the literature that address this problem, we propose here the land use recognition by means of Optimum-Path Forest Clustering (OPF), which has never been applied to this context up to date. Experiments among Optimum-Path Forest, Mean Shift and K-Means demonstrated the robustness of OPF for automatic land use classification of images obtained by CBERS-2B and Ikonos-2 satellites. © 2011 IEEE.
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Land use, mean shift, optimum-path forest, unsupervised classification, K-means, Landuse classifications, Mean shift, Unsupervised classification, Geology, Remote sensing
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International Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.