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Object-Oriented Crop Classification Using Time Series Sentinel Images from Google Earth Engine

文献类型： 外文期刊

作者： Xue, Hanyu 1 ; Xu, Xingang 1 ; Zhu, Qingzhen 2 ; Yang, Guijun 1 ; Long, Huiling 1 ; Li, Heli 1 ; Yang, Xiaodong 1 ; Zhang, Jianmin 3 ; Yang, Yongan 3 ; Xu, Sizhe 1 ; Yang, Min 1 ; Li, Yafeng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

3.Tianjin Dev & Demonstrat Ctr High Qual Agr Prod, Tianjin 301508, Peoples R China

关键词： Sentinel images; object-oriented; SNIC algorithm; random forest classification; support vector machine classification; Google Earth Engine

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 5 期

页码：

收录情况： SCI

摘要： The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification based on time series Sentinel images and object-oriented methods and takes the crop recognition and classification of the National Modern Agricultural Industrial Park in Jalaid Banner, Inner Mongolia, as the research object. It uses the Google Earth Engine (GEE) cloud platform to extract time series Sentinel satellite radar and optical remote sensing images combined with simple noniterative clustering (SNIC) multiscale segmentation with random forest (RF) and support vector machine (SVM) classification algorithms to classify and identify major regional crops based on radar and spectral features. Compared with the pixel-based method, the combination of SNIC multiscale segmentation and random forest classification based on time series radar and optical remote sensing images can effectively reduce the salt-and-pepper phenomenon in classification and improve crop classification accuracy with the highest accuracy of 98.66 and a kappa coefficient of 0.9823. This study provides a reference for large-scale crop identification and classification work.

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