中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

文献类型：期刊论文

作者Yin, Leikun1,2; You, Nanshan3; Zhang, Geli2; Huang, Jianxi2; Dong, Jinwei3
刊名REMOTE SENSING
出版日期2020
卷号12期号:1页码:20
关键词crop mapping feature selection spectro-temporal feature separability index Sentinel-2 Random Forest
DOI10.3390/rs12010162
通讯作者Zhang, Geli(geli.zhang@cau.edu.cn)
英文摘要Accurate crop planting area information is of significance for understanding regional food security and agricultural development planning. While increasing numbers of medium resolution satellite imagery and improved classification algorithms have been used for crop mapping, limited efforts have been made in feature selection, despite its vital impacts on crop classification. Furthermore, different crop types have their unique spectral and phenology characteristics; however, the different features of individual crop types have not been well understood and considered in previous studies of crop mapping. Here, we examined an optimized strategy to integrate specific features of individual crop types for mapping an improved crop type layer in the Sanjiang Plain, a new food bowl in China, by using all Sentinel-2 time series images in 2018. First, an automatic spectro-temporal feature selection (ASTFS) method was used to obtain optimal features for individual crops (rice, corn, and soybean), including sorting all features by the global separability indices for each crop and removing redundant features by accuracy changes when adding new features. Second, the ASTFS-based optimized feature sets for individual crops were used to produce three crop probability maps with the Random Forest classifier. Third, the probability maps were then composited into the final crop layer by considering the probability of each crop at every pixel. The resultant crop layer showed an improved accuracy (overall accuracy = 93.94%, Kappa coefficient = 0.92) than the other classifications without such a feature optimizing process. Our results indicate the potential of the ASTFS method for improving regional crop mapping.
WOS关键词TIME-SERIES DATA ; RANDOM FOREST ; SPECTRAL INDEXES ; AREA ESTIMATION ; MODIS ; CLASSIFICATION ; LANDSAT ; YIELD ; CORN ; AGRICULTURE
资助项目National Natural Science Foundation of China[41871349] ; National Natural Science Foundation of China[41671418] ; National Natural Science Foundation of China[41971383] ; Key Research Program of Frontier Sciencesof the Chinese Academy of Sciences (CAS)[QYZDB-SSW-DQC005] ; Thousand Youth Talents Plan ; Strategic Priority Research Program[XDA19040301]
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000515391700162
出版者MDPI
资助机构National Natural Science Foundation of China ; Key Research Program of Frontier Sciencesof the Chinese Academy of Sciences (CAS) ; Thousand Youth Talents Plan ; Strategic Priority Research Program
源URL[http://ir.igsnrr.ac.cn/handle/311030/132571]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Geli
作者单位1.Shandong Agr Univ, Sch Informat Sci & Engn, Tai An 271018, Shandong, Peoples R China
2.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Leikun,You, Nanshan,Zhang, Geli,et al. Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping[J]. REMOTE SENSING,2020,12(1):20.
APA Yin, Leikun,You, Nanshan,Zhang, Geli,Huang, Jianxi,&Dong, Jinwei.(2020).Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping.REMOTE SENSING,12(1),20.
MLA Yin, Leikun,et al."Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping".REMOTE SENSING 12.1(2020):20.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。