| 基于时序Landsat遥感数据的新疆开孔河流域农作物类型识别 |
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| 引用本文： | 汪小钦,邱鹏勋,李娅丽,茶明星.基于时序Landsat遥感数据的新疆开孔河流域农作物类型识别[J].农业工程学报,2019,35(16):180-188. |
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| 作者姓名： | 汪小钦 邱鹏勋 李娅丽 茶明星 |
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| 作者单位： | 1. 福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108； 2. 卫星空间信息技术综合应用国家地方联合工程研究中心，福州 350108; 3.数字中国研究院<福建>，福州 350108,1. 福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108； 2. 卫星空间信息技术综合应用国家地方联合工程研究中心，福州 350108; 3.数字中国研究院<福建>，福州 350108,1. 福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108； 2. 卫星空间信息技术综合应用国家地方联合工程研究中心，福州 350108; 3.数字中国研究院<福建>，福州 350108,1. 福州大学空间数据挖掘与信息共享教育部重点实验室，福州 350108； 2. 卫星空间信息技术综合应用国家地方联合工程研究中心，福州 350108; 3.数字中国研究院<福建>，福州 350108 |
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| 基金项目： | 国家重点研发计划课题（No. 2017YFB0504203）；中央引导地方发展专项（No. 2017L3012） |
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| 摘 要： | 快速、准确地获取农作物类别信息对农业部门的生产管理、政策制定具有重要作用。目前基于时间序列数据进行农作物分类主要是采用长时间序列的中低分辨率影像,大量的混合像元限制了农作物的分类精度。在农作物分类的特征选择方面主要是采用归一化植被指数(normalized differential vegetation index, NDVI),而其他特征量的应用还相对较少。该文以新疆开孔河农业区为研究区域,利用2016年的Landsat7 ETM+、Landsat8 OLI影像数据集,基于时间加权的动态时间规整(time weighted dynamic time warping,TWDTW)方法开展农作物类型识别研究,主要包括香梨、小麦、辣椒、棉花等。根据野外采集的样本点构建主要农作物的NDVI和第一主成分(principal component analysis 1,PCA1)时间序列,以反映不同农作物间的物候差异。基于NDVI数据分别利用DTW和TWDTW算法计算各未知像元序列与标准序列间的相似性程度,得到农作物的分类结果,2种方法的分类精度分别为65.69%、82.68%,表明时间权重的加入提高了DTW算法识别不同农作物的能力。结合NDVI与PCA1后,TWDTW的分类精度又提高了2.61个百分点,部分农作物的误分现象明显减少,说明PCA1能够进一步扩大作物间的差异性,提高分类精度。同时,还通过选取有限时相的影像组合进行分类,试验结果表明TWDTW算法在中高分辨率数据较少的情况下能够得到较为满意的分类结果,说明TWDTW算法在中高分辨率影像越来越丰富的时代具有应用潜力。
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| 关 键 词： | 遥感 作物 分类 时间加权的动态时间规整（TWDTW） 时间序列 归一化植被指数 主成分变换 |
| 收稿时间： | 2018/12/27 0:00:00 |
| 修稿时间： | 2019/7/16 0:00:00 |
Crops identification in Kaikong River Basin of Xinjiang based on time series Landsat remote sensing images |
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| Wang Xiaoqin,Qiu Pengxun,Li Yali and Cha Mingxing.Crops identification in Kaikong River Basin of Xinjiang based on time series Landsat remote sensing images[J].Transactions of the Chinese Society of Agricultural Engineering,2019,35(16):180-188. |
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| Authors: | Wang Xiaoqin Qiu Pengxun Li Yali and Cha Mingxing |
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| Institution: | 1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China; 2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China; 3. Academy of Digital China , Fuzhou 350108, China,1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China; 2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China; 3. Academy of Digital China , Fuzhou 350108, China,1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China; 2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China; 3. Academy of Digital China , Fuzhou 350108, China and 1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China; 2. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China; 3. Academy of Digital China , Fuzhou 350108, China |
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| Abstract: | The crops information of area and planting distribution has a great influence on the production management and policy making of the agricultural sector. Obtaining this information timely and accurately is not only the main content of agricultural remote sensing, but also the important reference for adjusting planting and estimating crop yield. At present, crop classification based on time series data mainly adopts medium and low spatial resolution images with long time series, while there are a large number of mixed pixels in low and medium spatial resolution images, which limits the classification accuracy of crops. The Normalized vegetation Index (NDVI) is mainly used in the selection of features of crop classification, while the application of other features selection is relatively few. With the rapid development of remote sensing technology, medium and high spatial resolution remote sensing data is becoming more and more abundant. How to make full use of medium and high spatial resolution images for crop classification has great research significance. This paper uses the Landsat7 ETM+ and Landsat8 OLI time series datasets of 2016 to extract crops from Kaikong River agricultural area of Xinjiang based on time-weighted dynamic time warping. It mainly includes pear, wheat, pepper, cotton and so on. According to the sample points collected in the field investigation, the sample database was established, and the NDVI values and PCA1 values of all kinds of samples were extracted at different time phases, that the standard NDVI sequences and PCA1 sequences were generated. In this paper, three classification schemes were designed and compared to explore the effect of DTW method on the recognition ability of different crops by using medium and high spatial resolution time series images, and to evaluate the time weight factor and the influence of NDVI combined with the first principal component (PCA1) on the classification results of crops. The principle of DTW algorithm classification was to calculate the distance value between the pixel sequence to be divided and the standard sequence of each crop. The smaller the distance value is, the higher the similarity between the sequences is. The crop type of the pixel to be divided was determined by comparing the distance value. However, because of its flexibility, the algorithm is prone to abnormal matching, and the introduction of time weight can limit this phenomenon very well. In this paper, DTW and TWDTW algorithms were used to classify crops based on NDVI data, and the classification accuracy of the two methods were 65.69% and 82.68%, respectively. It showed that the addition of time weight factor could effectively avoid the abnormal matching phenomenon of DTW algorithm and improve the ability of the algorithm to identify different crops. With the combination of NDVI and PCA1, the classification accuracy of TWDTW had increased by 2.61percentage points, and the phenomenon of the misclassification had significantly reduced. It explained that PCA1 could further expand the difference between crops and improve the classification accuracy. The experimental results showed that the TWDTW algorithm could obtain a satisfactory classification result in the case of less high spatial resolution data. It proved that the TWDTW algorithm has great application potential in the time of more and more high- spatial resolution images, and provides a reference for fine identification of crops based on time series data. |
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| Keywords: | remote sensing crops classification time weighted dynamic time warping time series normalized vegetation index principal component analysis |
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