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.
- 相关文献
作者其他论文 更多>>
-
UssNet: a spatial self-awareness algorithm for wheat lodging area detection
作者：Zhang, Jun;Wu, Qiang;Duan, Fenghui;Liu, Cuiping;Xiong, Shuping;Ma, Xinming;Cheng, Jinpeng;Feng, Mingzheng;Dai, Li;Wang, Xiaochun;Yang, Hao;Yang, Guijun;Chang, Shenglong
关键词：Unmanned aerial vehicle; State space models; Wheat lodging area identification; Semantic segmentation
-
A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment
作者：Jia, Jiwen;Kang, Junhua;Gao, Xiang;Zhang, Borui;Yang, Guijun;Chen, Lin;Yang, Guijun
关键词：monocular depth estimation; CNN; vision transformer; forest environment; comparative study
-
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering
作者：Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao
关键词：tassel; 3D phenotyping; TreeQSM; genotyping; clustering
-
Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning
作者：Chen, Riqiang;Feng, Haikuan;Hu, Haitang;Chen, Riqiang;Ren, Lipeng;Yang, Guijun;Cheng, Zhida;Zhao, Dan;Zhang, Chengjian;Feng, Haikuan;Hu, Haitang;Yang, Hao;Chen, Riqiang;Zhang, Chengjian;Ren, Lipeng;Feng, Haikuan
关键词：maize; chlorophyll; radiative transfer model; feature selection; transfer learning
-
Field-scale irrigated winter wheat mapping using a novel cross-region slope length index in 3D canopy hydrothermal and spectral feature space
作者：Zhang, Youming;Yang, Guijun;Li, Zhenhong;Liu, Miao;Zhang, Jing;Gao, Meiling;Zhu, Wu;Zhang, Youming;Yang, Guijun;Yang, Xiaodong;Song, Xiaoyu;Long, Huiling;Liu, Miao;Meng, Yang;Thenkabail, Prasad S.;Wu, Wenbin;Zuo, Lijun;Meng, Yang
关键词：Winter wheat; Irrigation mapping; Hydrothermal and spectral feature; Cross-region; Rainfed line; Slope Length Index
-
Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (Annona squamosa L.)
作者：Jiang, Xiangtai;Xu, Xingang;Wu, Wenbiao;Yang, Guijun;Meng, Yang;Feng, Haikuan;Li, Yafeng;Xue, Hanyu;Chen, Tianen;Jiang, Xiangtai;Xu, Xingang;Gao, Lutao
关键词：canopy nitrogen content; UAV remote sensing; ensemble learning; Lasso model
-
Retrieving the chlorophyll content of individual apple trees by reducing canopy shadow impact via a 3D radiative transfer model and UAV multispectral imagery
作者：Zhang, Chengjian;Chen, Zhibo;Chen, Riqiang;Zhang, Wenjie;Zhang, Chengjian;Chen, Riqiang;Zhang, Wenjie;Zhao, Dan;Yang, Guijun;Xu, Bo;Feng, Haikuan;Yang, Hao
关键词：Chlorophyll content; Shadows; Vegetation index (VI); Radiative transfer models (RTMs); Hybrid inversion model; Individual apple tree crown
