Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 541-554 |
| Seitenumfang | 14 |
| Fachzeitschrift | Expert systems with applications |
| Jahrgang | 39 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 19 Juli 2011 |
| Publikationsstatus | Veröffentlicht - Jan. 2012 |
Abstract
This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Artificial intelligence
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in: Expert systems with applications, Jahrgang 39, Nr. 1, 01.2012, S. 541-554.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - An open source object-based framework to extract landform classes
AU - Camargo, F. F.
AU - Almeida, C. M.
AU - Costa, G. A.O.P.
AU - Feitosa, R. Q.
AU - Oliveira, D. A.B.
AU - Heipke, C.
AU - Ferreira, R. S.
N1 - Funding Information: The work reported in this paper has been jointly supported by the German Aerospace Agency (DLR) and the Brazilian National Council for Scientific Research (CNPq) under grant number 491084/2005-6. The authors acknowledge the financial support provided by the Rio de Janeiro Foundation for Scientific Research (FAPERJ) and the Brazilian Federal Agency for Scientific Projects and Research (FINEP) .
PY - 2012/1
Y1 - 2012/1
N2 - This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.
AB - This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.
KW - Cognitive approaches
KW - Geomorphology
KW - InterIMAGE
KW - Object-based image analysis
KW - Semantic network
UR - http://www.scopus.com/inward/record.url?scp=81855221792&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2011.07.044
DO - 10.1016/j.eswa.2011.07.044
M3 - Article
AN - SCOPUS:81855221792
VL - 39
SP - 541
EP - 554
JO - Expert systems with applications
JF - Expert systems with applications
SN - 0957-4174
IS - 1
ER -