NDLI: Ensemble of artificial neural network based land cover classifiers using satellite data

Content Provider	IEEE Xplore Digital Library
Author	Mackin, K.J. Yamaguchi, T. Nunohiro, E. Jong Geol Park Hara, K. Matsushita, K. Ohshiro, M. Yamasaki, K.
Copyright Year	2007
Description	Author affiliation: Tokyo Univ. of Inf. Sci., Chiba (Mackin, K.J.)
Abstract	Terra and Aqua, 2 satellites launched by the NASA-centered international Earth Observing System project, house MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Moderate resolution remote sensing allows the quantifying of land surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this paper, we propose applying an ensemble technique, based on fault masking among individual classifier for N-version programming. We create an N-version programming ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an N-version programming ensemble of neural networks greatly improves the classification error rate of land cover type.
Starting Page	1653
Ending Page	1657
File Size	287328
Page Count	5
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4414110
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Artificial satellites MODIS Remote monitoring Land surface Earth Observing System Image sensors Sensor systems Voting Error analysis
Content Type	Text
Resource Type	Article

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