Abstract
We consider a detection method of rice field under rainy conditions by using remote sensing data. The classification method is to use a competitive neural network of self-organizing feature map (SOM) by using remote sensing data observed before and after planting rice in Hiroshima, Japan. Three RADAR Satellites (RADARSAT) and one Satellite Pour l’Observation de la Terre(SPOT)/High Resolution Visible (HRV) data are used to detect rice field. Synthetic Aperture Radar (SAR) reflects back-scattering intensity in rice fields. The intensity decreases from April to May and increases from May to June. It is shown that the competitive neural network of self-organizing feature map is useful for the classification of the SAR data to find the area of rice fields.
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Omatu, S., Yano, M. (2015). Detection of Rice Field Using the Self-organizing Feature Map. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 12th International Conference. Advances in Intelligent Systems and Computing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-319-19638-1_4
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DOI: https://doi.org/10.1007/978-3-319-19638-1_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19637-4
Online ISBN: 978-3-319-19638-1
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