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Feature-Ensemble-Based Crop Mapping for Multi-Temporal Sentinel-2 Data Using Oversampling Algorithms and Gray Wolf Optimizer Support Vector Machine

文献类型： 外文期刊

作者： Zhang, Haitian 1 ; Gao, Maofang 1 ; Ren, Chao 2 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China

2.Guilin Univ Technol, Coll Geomat & Geoinformat, Guilin 530001, Peoples R China

关键词： Sentinel-2 data; crop mapping; multi-temporal; oversampling algorithms; GWO-SVM

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

ISSN：

年卷期： 2022 年 14 卷 20 期

页码：

收录情况： SCI

摘要： Accurate spatial distribution and area of crops are important basic data for assessing agricultural productivity and ensuring food security. Traditional classification methods tend to fit most categories, which will cause the classification accuracy of major crops and minor crops to be too low. Therefore, we proposed an improved Gray Wolf Optimizer support vector machine (GWO-SVM) method with oversampling algorithm to solve the imbalance-class problem in the classification process and improve the classification accuracy of complex crops. Fifteen feature bands were selected based on feature importance evaluation and correlation analysis. Five different smote methods were used to detect samples imbalanced with respect to major and minor crops. In addition, the classification results were compared with support vector machine (SVM) and random forest (RF) classifier. In order to improve the classification accuracy, we proposed a combined improved GWO-SVM algorithm, using an oversampling algorithm(smote) to extract major crops and minor crops and use SVM and RF as classification comparison methods. The experimental results showed that band 2 (B2), band 4 (B4), band 6 (B6), band 11 (B11), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) had higher feature importance. The classification results oversampling- based of smote, smote-enn, borderline-smote1, borderline-smote2, and distance-smote were significantly improved, with accuracy 2.84%, 2.66%, 3.94%, 4.18%, 6.96% higher than that those without 26 oversampling, respectively. At the same time, compared with SVM and RF, the overall accuracy of improved GWO-SVM was improved by 0.8% and 1.1%, respectively. Therefore, the GWO-SVM model in this study not only effectively solves the problem of equilibrium of complex crop samples in the classification process, but also effectively improves the overall classification accuracy of crops in complex farming areas, thus providing a feasible alternative for large-scale and complex crop mapping.

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