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