NDLI: Bayesian feature selection for classifying multi-temporal SAR and TM data

Content Provider	IEEE Xplore Digital Library
Author	Yamagata, Y. Oguma, H.
Copyright Year	1997
Description	Author affiliation: Nat. Inst. for Environ. Studies, Ibaraki, Japan (Yamagata, Y.)
Abstract	Remotely sensed imagery data from various satellite sensors are now available for environmental monitoring. However, due to the difficulty in surveying, it is not easy to obtain a sufficient number of training data for classifying these high dimensional imagery data. In order to make use of these imagery data, it is necessary to develop a classification method which can attain a high classification accuracy only using a limited number of training data. In this study, the authors have tested the Bayesian approaches which integrate feature selection and model averaging in the classification process. The experiments are conducted using bayesian neural networks, Gaussian process, and maximum likelihood for classifying wetland vegetation types using multi-temporal LANDAT/TM, JERS1/SAR, and ERS/SAR data. The results shows that the Bayesian approaches work well for classifying these imagery data, and especially the Gaussian process has a very high accuracy which outperforms other methods for classifying the sensor fusion data using JERS1/SAR and LANDSAT/TM.
Starting Page	978
Ending Page	980
File Size	272515
Page Count	3
File Format	PDF
ISBN	0780338367
DOI	10.1109/IGARSS.1997.615316
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-08-03
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Satellites Training data Gaussian processes Image sensors Remote monitoring Testing Neural networks Vegetation mapping Sensor fusion
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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