Rice-Planted Area Extraction by RADARSAT Data Using Learning Vector Quantization Algorithm
Rice-Planted Area Extraction by RADARSAT Data Using Learning Vector Quantization Algorithm
复制标题
使用学习矢量量化算法通过 RADARSAT 数据提取水稻种植面积
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Omatu
中科院分区：
文献类型：
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作者：
S. Omatu
基于神经网络的分类技术是近年来发展起来的。本文应用学习向量量化（LVQ）神经网络对包括微波和光学传感器在内的遥感数据进行分类，用于稻田估计。该方法具有通过学习确定的非线性判别函数的能力。卫星数据是1999年水稻种植前后的观测数据。三个RADARSAT和一个SPOT/HRV数据在日本广岛使用。RADARSAT图像只有一个波段数据，很难提取稻田。稻田SAR后向散射强度在4 ~ 5月呈下降趋势，在5 ~ 6月呈上升趋势。因此，本研究使用了4月至6月的三幅RADARSAT图像。将LVQ分类方法应用于RADARSAT和SPOT数据，对稻田估算结果进行评价。结果表明，与SPOT数据相比，利用LVQ估算RADASAT数据稻田的真实产量约为60%。结果表明，该方法与基于最大似然（MLH）的SAR图像分类方法相比，具有较好的分类效果。
The classification technique using the neural net- works 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 back- scattering 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.