Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X
Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X
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DOI：
10.1080/01431161.2014.978038
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发表时间：
2014-12
影响因子：
3.4
通讯作者：
Rei Sonobe;H. Tani;Xiufeng Wang;N. Kobayashi;H. Shimamura
中科院分区：
文献类型：
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作者：
Rei Sonobe;H. Tani;Xiufeng Wang;N. Kobayashi;H. Shimamura
本文介绍了在日本北海道使用TerraSAR-X数据进行作物制图的三种不同分类算法的比较。研究区种植了豆类、甜菜、草地、玉米、土豆和冬小麦。虽然农业灾害补偿的管理和估计都需要分类图，但这些技术尚未建立。一些监督学习模型可能允许精确的分类。因此，对分类回归树（CART）、支持向量机（SVM）和随机森林（RF）进行比较。SVM是本研究中最优算法，同年分类的总体准确率为89.1%，即使用2009年训练数据对2009年测试数据进行分类；跨年分类的总体准确率为78.0%，即使用2009年训练数据对2012年数据进行分类。
This article describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X data. In the study area, beans, beets, grasslands, maize, potatoes, and winter wheat were cultivated. Although classification maps are required for both management and estimation of agricultural disaster compensation, those techniques have yet to be established. Some supervised learning models may allow accurate classification. Therefore, comparisons among the classification and regression tree (CART), the support vector machine (SVM), and random forests (RF) were performed. SVM was the optimum algorithm in this study, achieving an overall accuracy of 89.1% for the same-year classification, which is the classification using the training data in 2009 to classify the test data in 2009, and 78.0% for the cross-year classification, which is the classification using the training data in 2009 to classify the data in 2012.