Accessing the Temporal and Spectral Features in Crop Type Mapping using Multi-temporal Sentinel-2 Imagery: A Case Study of Yi'an County, Heilongjiang Province, China
Accessing the Temporal and Spectral Features in Crop Type Mapping using Multi-temporal Sentinel-2 Imagery: A Case Study of Yi'an County, Heilongjiang Province, China
复制标题
使用多时相 Sentinel-2 影像获取作物类型制图中的时空和光谱特征：以中国黑龙江省依安县为例
DOI：
10.1016/j.compag.2020.105618
复制
发表时间：
2020-09
影响因子：
8.3
通讯作者：
Liangpei Zhang
中科院分区：
文献类型：
--
作者：
Hongyan Zhang;Jinzhong Kang;Xiong Xu;Liangpei Zhang
作物类型制图直观地展示了不同作物类型耕地面积的空间分布格局和比例，是后续农业应用的基础。了解不同时间和光谱特征在详细作物分类中的有效性可以帮助用户在作物类型绘图应用中优化时间窗口选择和光谱特征空间构建。因此，在本研究中，我们使用来自中国黑龙江省依安县的时间序列Sentinel-2图像数据来分析三种常见机器学习分类方法：分类和回归树（CART）决策树、支持向量机（SVM）和随机森林（RF）中使用的时间和光谱特征的有效性。对于CART和SVM分类器，特征的相对重要性通过选择作为节点的属性的顺序和频率以及模型权重的平方来反映。在 RF 中，通过袋外数据计算出的预测误差的变化被视为特征重要性的度量。所有标记像素平均值的标准差用于评估这三种方法得出的一致结论的正确性。混淆矩阵给出的定量评价结果表明，随机森林取得了最好的总体准确率，支持向量机排名第二，决策树算法产生了最不准确的分类结果。从特征重要性的角度来看，充分利用不同作物之间的判别信息，构建合理的特征空间，有助于显着提高分类精度。具体来说，不同作物类型之间的判别信息如下： 1）作物生长高峰期的图像对于不同作物的分类至关重要； 2）短波红外波段特别适合作物精细分类； 3）红边带可以有效辅助分类。最终，我们的研究实现了研究区的作物类型制图，总体精度为97.85%，Kappa系数为0.95。
Crop type mapping visualizes the spatial distribution patterns and proportions of the cultivated areas with different crop types, and is the basis for subsequent agricultural applications. Understanding the effectiveness of different temporal and spectral features in detailed crop classification can help users optimize temporal window selection and spectral feature space construction in crop type mapping applications. Therefore, in this study, we used time-series Sentinel-2 image data from Yi’an County, Heilongjiang province, China, to analyze the effectiveness of the temporal and spectral features used in three common machine learning classification methods: classification and regression tree (CART) decision tree, Support Vector Machine (SVM), and random forest (RF). For CART and SVM classifiers, the relative importance of the features was reflected by the order and frequency of attributes selected as the node and the square of the model weight. In RF, the change in prediction error as calculated by out of bag data is taken as the measure of feature importance. The standard deviation of the average value of all labeled pixels was used to evaluate the correctness of the unanimous conclusions drawn by these three methodologies. The quantitative evaluation results given by the confusion matrix show that random forest achieved the best overall accuracy, while support vector machine ranked second, and the decision tree algorithm yielded the least accurate classification results. From the perspective of feature importance, making full use of the discriminative information between different crops, and constructing a rational feature space, can help to improve classification accuracy significantly. In detail, the discriminative information between the different crop types is as follows: 1) images at the peak of the crop growth period are crucial in the classification of different crops; 2) the short-wave infrared bands are particularly suitable for fine crop classification; and 3) the red edge bands can effectively assist classification. Finally, our study achieved crop type mapping in the study area with an overall accuracy of 97.85% and a Kappa coefficient of 0.95.
登录
查看更多内容
采用全变分正则化和非局部低阶张量分解的高光谱图像去噪
影响因子：
8.2
作者：
Hongyan Zhang;Lu Liu;Wei He;Liangpei Zhang
通讯作者：
Liangpei Zhang
DOI：
10.1016/j.jag.2015.02.005
发表时间：
2015-07
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
--
作者：
Rami Piiroinen;J. Heiskanen;M. Mõttus;P. Pellikka
通讯作者：
Rami Piiroinen;J. Heiskanen;M. Mõttus;P. Pellikka
影响因子：
2.1
作者：
B. Gelder;A. Kaleita;R. Cruse
通讯作者：
B. Gelder;A. Kaleita;R. Cruse
影响因子：
13.5
作者：
Wang, Sherrie;Azzari, George;Lobell, David B.
通讯作者：
Lobell, David B.
DOI：
--
发表时间：
1992-10
期刊：
--
影响因子：
--
作者：
J. R. Quinlan
通讯作者：
J. R. Quinlan