A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
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DOI：
10.1016/j.jag.2014.07.002
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
B. Zheng;S. Myint;P. Thenkabail;R. Aggarwal
中科院分区：
文献类型：
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作者：
B. Zheng;S. Myint;P. Thenkabail;R. Aggarwal
许多农业环境评估都需要特定地点的作物类型信息。研究了支持向量机在凤凰城主动管理区复杂种植系统中区分不同作物类型的潜力。我们将支持向量机应用于Landsat时间序列归一化差异植被指数(NDVI)数据，使用两种不同的方法选择训练数据集：分层随机方法和基于局部知识的智能选择方法。支持向量机模型有效地对九种主要作物类型进行了分类，两个训练数据集的总体准确率都达到了86%。结果表明，与分层随机方法相比，智能选择方法能够减少训练集的规模，并获得更高的总体分类精度。当参考数据的可获得性有限且不同类别之间不平衡时，智能选择方法特别有用。这项研究证明了利用多时相陆地卫星图像在干旱和半干旱地区系统监测作物类型和种植模式的潜力。
Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions.