Land use image classification through Optimum-Path Forest Clustering

Author

Pisani, R. ; Riedel, P. ; Ferreira, M. ; Marques, M. ; Mizobe, R. ; Papa, J.

Author_Institution

Geosci. & Exact Sci. Inst., UNESP - Univ. Estadual Paulista, Paulista, Brazil

fYear

2011

fDate

24-29 July 2011

Firstpage

826

Lastpage

829

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.

Keywords

geophysical image processing; image classification; terrain mapping; vegetation mapping; CBERS-2B satellite; Ikonos-2 satellite; OPF clustering; automatic land use classification; deforesting area monitoring; illegal land use; k-means clustering comparison; land use image classification; land use recognition; mean shift clustering comparison; optimum path forest clustering; Algorithm design and analysis; Clustering algorithms; Geology; Remote sensing; Roads; Robustness; Satellites; Land use; mean shift; optimum-path forest; unsupervised classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6049258

Filename

6049258