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Rice-Planted Area Extraction by RADARSAT Data Using Learning Vector Quantization Algorithm

Authors:
Sigeru Omatu

Keywords: Remote sensing; Synthetic aperture radar; Neural networks; Learning vector quantization; Maximum likelihood method

Abstract:
The classification technique using the neural networks has been recently developed. We apply a neural network of Learning Vector Quantization (LVQ) to classify remote sensing data including microwave and optical sensors for estimation of a rice field. The method has capability of a nonlinear discrimination function which is determined by learning. The satellite data were observed before and after planting rice in 1999. Three RADARSAT and one SPOT/HRV data are used in Higashi- Hiroshima City, Japan. RADARSAT image has only one band data, which is difficult to extract a rice field. However, SAR backscattering intensity in a rice field decreases from April to May and increases from May to June. Thus, three RADARSAT images from April to June are used for this study. The LVQ classification was applied to RADARSAT and SPOT data in order to evaluate rice field estimation. The results show that the true production rate of rice field estimation for RADASAT data by using LVQ was approximately 60% compared with SPOT data. It is shown that the present method is much better compared with SAR image classification by the maximum likelihood (MLH) method.

Pages: 19 to 23

Copyright: Copyright (c) IARIA, 2013

Publication date: September 29, 2013

Published in: conference

ISSN: 2308-4499

ISBN: 978-1-61208-290-5

Location: Porto, Portugal

Dates: from September 29, 2013 to October 3, 2013