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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/72802
Title: 
Land use image classification through optimum-path forest clustering
Author(s): 
Institution: 
Universidade Estadual Paulista (UNESP)
Abstract: 
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.
Issue Date: 
16-Nov-2011
Citation: 
International Geoscience and Remote Sensing Symposium (IGARSS), p. 826-829.
Time Duration: 
826-829
Keywords: 
  • Land use
  • mean shift
  • optimum-path forest
  • unsupervised classification
  • K-means
  • Landuse classifications
  • Mean shift
  • Unsupervised classification
  • Geology
  • Remote sensing
Source: 
http://dx.doi.org/10.1109/IGARSS.2011.6049258
URI: 
Access Rights: 
Acesso restrito
Type: 
outro
Source:
http://repositorio.unesp.br/handle/11449/72802
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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