Extraction rice-planted areas by RADARSAT data using neural networks |
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Authors: | Tomohisa Konishi Sigeru Omatu Yuzo Suga |
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Affiliation: | (1) Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan;(2) Department of Global Environment Studies, Hiroshima Institute of Technology, Hiroshima, Japan |
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Abstract: | A classification technique using the neural networks has recently been developed. We apply a neural network of learning vector
quantization (LVQ) to classify remote-sensing data, including microwave and optical sensors, for the estimation of a rice-planted
area. The method has the capability of nonlinear discrimination, and the classification function is determined by learning.
The satellite data were observed before and after planting rice in 1999. Three sets of RADARSAT and one set of SPOT/HRV data
were used in Higashi–Hiroshima, Japan. Three RADARSAT images from April to June were used for this study. The LVQ classification
was applied the RADARSAT and SPOT to evaluate the estimate of the area of planted-rice. The results show that the true production
rate of the rice-planted area estimation of RADASAT by LVQ was approximately 60% compared with that of SPOT by LVQ. It is
shown that the present method is much better than the SAR image classification by the maximum likelihood method. |
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Keywords: | Remote sensing Synthetic aperture radar Learning vector quantization |
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