A Comparative Analysis of Random Forest and Support Vector Machines for Classifying Irrigated Cropping Areas in The Upper-Comoé Basin, Burkina Faso

Indian Journal Of Science And Technology Pub Date : 2024-02-15 DOI:10.17485/ijst/v17i8.78

Farid Traoré, Sié Palé, Aïda Zaré, Moussa Karamoko Traoré, Blaise Ouédraogo, J. Bonkoungou

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Abstract

Objectives: This study investigates the performance of two machine-learning algorithms in classifying land areas across the Upper-Comoé basin in Burkina Faso. Methods: Within the Google Earth Engine data processing environment, Support Vector Machine (SVM) and the Random Forest (RF) algorithms were applied to a Landsat-8 OLI image of March 2019, to discriminate agricultural land areas, with an emphasis on irrigated areas. Findings: The results indicated good to excellent classification performance, with overall accuracies and Kappa coefficients between 71% and 99%, and 0.66 and 0.99, respectively. The RF method outperformed the SVM in terms of mapping "accuracy", but in terms of spatial distribution of classes, the SVM method provided a mapping close to reality, due to the density of the classes generated. Novelty: Our findings suggest that remote sensing can constitute a tool fully adapted to the needs of services in charge of agricultural water management in Burkina Faso. Keywords: Irrigation, Random Forest, Support Vector Machine, Google Earth Engine, Burkina Faso

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随机森林和支持向量机在布基纳法索上科莫埃盆地灌溉种植区分类中的比较分析

研究目的本研究调查了两种机器学习算法在布基纳法索上科莫埃盆地土地区域分类中的表现。研究方法在谷歌地球引擎数据处理环境中，将支持向量机（SVM）和随机森林（RF）算法应用于 2019 年 3 月的 Landsat-8 OLI 图像，以区分农田区域，重点是灌溉区域。研究结果结果表明分类性能良好至卓越，总体准确率和 Kappa 系数分别在 71% 和 99% 之间，以及 0.66 和 0.99 之间。就绘图 "准确性 "而言，RF 方法优于 SVM 方法，但就类别的空间分布而言，SVM 方法提供的绘图接近实际情况，这是因为生成的类别密度较大。新颖性：我们的研究结果表明，遥感技术可以成为一种完全适应布基纳法索农业用水管理服务需求的工具。关键词灌溉、随机森林、支持向量机、谷歌地球引擎、布基纳法索

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Indian Journal Of Science And Technology

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