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Classification of cultivated rice fields in northern Vietnam using polarimetric RADARSAT-2 data

机译:利用极化RADARSAT-2数据对越南北部的稻田进行分类

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This paper presents two classification schemes to identify rice fields in a complex land-use watershed in northern Vietnam: Thresholding and Support Vector Machine (SVM) classification algorithm. The data used are C-band dual-polarization (HH, HV) and polarimetric (quad-pol) data from RADARSAT-2. Two questions are posed: firstly, what is the usefulness of different polarizations (HH and HV) for rice detection? Secondly, between different polarimetric parameters, which ones are the most suitable to separate the rice fields from others? The analysis shows that the rice intensity values increase in the middle of the crop season and are different from other vegetation types in HH polarization. In parallel, the coherence (T) matrix seems a suitable polarimetric parameter for rice extraction. Results suggest that both HH polarization intensity values and quadpol data could identify rice fields at regional scale with a precision of 71% and 80%.
机译:本文提出了两种识别越南北部复杂土地利用流域中稻田的分类方案:阈值和支持向量机(SVM)分类算法。使用的数据是来自RADARSAT-2的C波段双极化(HH,HV)和极化(quad-pol)数据。提出了两个问题:首先,不同极化(HH和HV)对稻米检测有什么用?其次,在不同的极化参数之间,哪个参数最适合将稻田与其他稻田区分开?分析表明,稻米强度值在作物生长季的中段有所增加,并且在HH极化过程中不同于其他植被类型。同时,相干(T)矩阵似乎是水稻提取的合适极化参数。结果表明,HH极化强度值和quadpol数据均可以以71%和80%的精度识别区域规模的稻田。

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