A Novel Feature for Detection of Rice Field Distribution Using Time Series SAR Data
A Novel Feature for Detection of Rice Field Distribution Using Time Series SAR Data
复制标题
利用时间序列 SAR 数据检测稻田分布的新功能
DOI：
10.1109/igarss39084.2020.9323278
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Meng
中科院分区：
文献类型：
--
作者：
Lena Chang;Yi;Yang;Meng
稻米是许多国家最重要的食物来源，特别是亚洲，包括台湾。监测稻田分布可以有效管理粮食安全。提出了一种基于特征的决策方法，利用Sentinel-1A提供的时间序列合成孔径雷达（SAR）数据检测水稻种植的映射。与以往文献中使用SAR数据的最大和最小后向散射不同，本研究基于水稻生长期的完整时间序列数据建立了水稻生长模型。在此基础上，引入了与水稻生长时间相关的一个特征，即营养生长期与成熟期的时间间隔。首先将该特征与SAR数据最大值和最小值之间的后向散射差（BD）特征进行了比较。实验结果表明，该特征能够获得较好的大米检测精度。在此基础上，提出了一种基于TI和BD特征相结合的水稻种植决策方法。本研究以中部云林县与昌化县为实验区。实验结果表明，该方法在水稻VH偏振检测中的总体准确率可达90%以上。与传统的基于生长高度特征的检测方法BD相比，该方法可以提高水稻检测的整体准确率约5%。
Rice is the most important food source for many countries, which especially for Asia, includes Taiwan. Monitoring rice field distribution can effectively manage food security. This study proposed a feature-based decision approach to detect the mapping of rice cultivation using the time-series Synthetic Aperture Radar (SAR) data provided by Sentinel-1A. Instead of using the maximum and minimum backscatter of SAR data, as most studies in the literature, this study established a rice growth model based on complete time series data in the rice growth period. From the developed model, a feature related to rice growth time, that is, the time interval (TI) between vegetative growth and maturity stages was introduced. The proposed feature was first compared with the feature of backscatter difference (BD) between the maximum and minimum value of SAR data. The experimental results show that the proposed feature can achieve better rice detection accuracy. Then, a decision method based on the combination of TI and BD features was proposed for rice planting mapping. In the study, Yunlin and Changhua counties in central Taiwan were used as experimental areas. The experimental results show that the proposed method can achieve more than 90% overall accuracy in rice detection for VH polarization. Furthermore, comparing with the traditional method that uses growth height feature, BD, the proposed method can improve the overall accuracy of rice detection about 5%.