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Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model

文献类型：期刊论文

作者Wang, Xiaoping1,6; Zhang, Fei2,6; Kung, Hsiang-te5; Johnson, Verner Carl4; Latif, Aamir3
刊名INTERNATIONAL JOURNAL OF REMOTE SENSING
出版日期2019-08-16
页码21
ISSN号0143-1161
DOI10.1080/01431161.2019.1654142
通讯作者Zhang, Fei(zhangfei3s@163.com)
英文摘要The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (kappa) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions.
WOS关键词NATIONAL NATURE-RESERVE ; EBINUR LAKE ; SPATIOTEMPORAL FUSION ; LAND-COVER ; CLASSIFICATION ; SALINITY ; CHINA ; SYSTEM ; IMAGES ; BASIN
资助项目Xinjiang Local Outstanding Young Talent Cultivation Project of the National Natural Science Foundation of China[U1503302] ; National Natural Science Foundation of China[41361045] ; Tianshan talent project of Xinjiang Uygur Autonomous region[400070010209]
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000480919800001
出版者TAYLOR & FRANCIS LTD
资助机构Xinjiang Local Outstanding Young Talent Cultivation Project of the National Natural Science Foundation of China ; National Natural Science Foundation of China ; Tianshan talent project of Xinjiang Uygur Autonomous region
源URL[http://ir.igsnrr.ac.cn/handle/311030/68941]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Fei
作者单位1.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Jiangsu, Peoples R China
2.Xinjiang Univ, Minist Educ, Key Lab Oasis Ecol, Urumqi, Peoples R China
3.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.Colorado Mesa Univ, Dept Phys & Environm Sci, Grand Junction, CO USA
5.Univ Memphis, Dept Earth Sci, Memphis, TN 38152 USA
6.Xinjiang Univ, Coll Resources & Environm Sci, Key Lab Smart City & Environm Modelling Higher Ed, Urumqi 830046, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xiaoping,Zhang, Fei,Kung, Hsiang-te,et al. Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019:21.
APA Wang, Xiaoping,Zhang, Fei,Kung, Hsiang-te,Johnson, Verner Carl,&Latif, Aamir.(2019).Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model.INTERNATIONAL JOURNAL OF REMOTE SENSING,21.
MLA Wang, Xiaoping,et al."Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model".INTERNATIONAL JOURNAL OF REMOTE SENSING (2019):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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