Details
| Original language | English |
|---|---|
| Pages (from-to) | 619-629 |
| Number of pages | 11 |
| Journal | Photogrammetric Engineering and Remote Sensing |
| Volume | 77 |
| Issue number | 6 |
| Publication status | Published - Jun 2011 |
Abstract
This research is committed to develop a semi-automated landforms classification method for a subtropical area located in the southeast of Brazil, using optical mediumresolution imagery from ASTER/Terra. A four-level semantic network driven by a set of spectral, textural, and geomorphometric variables was used. The textural and geomorphometric variables were extracted from an ASTER/Terra DEM. The semantic network was initially conceived to classify macro morphogenetic landforms and was then further detailed to allow a finer classification, which amounted to eleven classes of morphographic landforms. In order to assess the classification accuracy, statistical indices were derived from a contingency table obtained by means of a comparison between the classified scene and a reference map. The final agreement indices for the macro and detailed landforms classifications were 76 percent and 80 percent, respectively. The employed object-based image analysis has proved to be a suitable method for semiautomated procedures in the classification of landforms.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: Photogrammetric Engineering and Remote Sensing, Vol. 77, No. 6, 06.2011, p. 619-629.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - ASTER/Terra Imagery and a Multilevel Semantic Network for Semi-automated Classification of Landforms in a Subtropical Area
AU - Camargo, F. F.
AU - Almeida, C. M.
AU - Florenzano, T. G.
AU - Heipke, C.
AU - Feitosa, R. Q.
AU - Costa, G. A.O.P.
N1 - Funding Information: The work reported in this paper has been jointly supported by the Brazilian National Council for Scientific Research (CNPQ) and the German Aerospace Agency (DLR) under Grant Number 491084/2005-6.
PY - 2011/6
Y1 - 2011/6
N2 - This research is committed to develop a semi-automated landforms classification method for a subtropical area located in the southeast of Brazil, using optical mediumresolution imagery from ASTER/Terra. A four-level semantic network driven by a set of spectral, textural, and geomorphometric variables was used. The textural and geomorphometric variables were extracted from an ASTER/Terra DEM. The semantic network was initially conceived to classify macro morphogenetic landforms and was then further detailed to allow a finer classification, which amounted to eleven classes of morphographic landforms. In order to assess the classification accuracy, statistical indices were derived from a contingency table obtained by means of a comparison between the classified scene and a reference map. The final agreement indices for the macro and detailed landforms classifications were 76 percent and 80 percent, respectively. The employed object-based image analysis has proved to be a suitable method for semiautomated procedures in the classification of landforms.
AB - This research is committed to develop a semi-automated landforms classification method for a subtropical area located in the southeast of Brazil, using optical mediumresolution imagery from ASTER/Terra. A four-level semantic network driven by a set of spectral, textural, and geomorphometric variables was used. The textural and geomorphometric variables were extracted from an ASTER/Terra DEM. The semantic network was initially conceived to classify macro morphogenetic landforms and was then further detailed to allow a finer classification, which amounted to eleven classes of morphographic landforms. In order to assess the classification accuracy, statistical indices were derived from a contingency table obtained by means of a comparison between the classified scene and a reference map. The final agreement indices for the macro and detailed landforms classifications were 76 percent and 80 percent, respectively. The employed object-based image analysis has proved to be a suitable method for semiautomated procedures in the classification of landforms.
UR - http://www.scopus.com/inward/record.url?scp=79960384003&partnerID=8YFLogxK
U2 - 10.14358/PERS.77.6.619
DO - 10.14358/PERS.77.6.619
M3 - Article
AN - SCOPUS:79960384003
VL - 77
SP - 619
EP - 629
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
SN - 0099-1112
IS - 6
ER -