DocumentCode :
2208081
Title :
Probabilistic land cover classification approach toward knowledge-based satellite data interpretations
Author :
Hashimoto, Shutaro ; Tadono, Takeo ; Onosato, Masahiko ; Hori, Masahiro ; Moriyama, Takashi
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
fYear :
2012
fDate :
22-27 July 2012
Firstpage :
1513
Lastpage :
1516
Abstract :
The recognition of concepts that we human beings are able to locate within satellite imagery requires analysis based on the particular context using knowledge. In this paper, we present a supervised pixel-based classification approach toward utilization of the classification results in knowledgebased satellite data interpretation system. The proposed approach is based upon a generative model, which is able to output the classification results with their probabilities and subsequently utilize them in detailed analysis. The experiment of classification was performed to demonstrate characteristics of the approach.
Keywords :
geophysical image processing; image classification; knowledge based systems; terrain mapping; classification results; generative model; knowledge based satellite data interpretation system; knowledge based satellite data interpretations; probabilistic land cover classification approach; satellite imagery; supervised pixel based classification approach; Machine learning; Probabilistic logic; Remote sensing; Satellites; Support vector machine classification; Training; Vectors; generative model; knowledge-based system; land cover classification; machine learning; probabilistic inference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
Type :
conf
DOI :
10.1109/IGARSS.2012.6351247
Filename :
6351247
Link To Document :
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