Planetscope Nanosatellites Image Classification Using Machine Learning

作     者：Mohd Anul Haq 

作者机构：Department of Computer ScienceCollege of Computer and Information SciencesMajmaah UniversityALMajmaah11952Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第42卷第9期

页      面：1031-1046页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Ministry of Education in Saudi Arabia  (IFP-2020–14) 

主　　题：Planetscope nanosatellites classification logistic regression computer vision 

摘      要：To adopt sustainable crop practices in changing climate, understandingthe climatic parameters and water requirements with vegetation is crucial on aspatiotemporal scale. The Planetscope (PS) constellation of more than 130 nanosatellites from Planet Labs revolutionize the high-resolution vegetation assessment. PS-derived Normalized Difference Vegetation Index (NDVI) maps areone of the highest resolution data that can transform agricultural practices andmanagement on a large scale. High-resolution PS nanosatellite data was utilizedin the current study to monitor agriculture’s spatiotemporal assessment for theAl-Qassim region, Kingdom of Saudi Arabia (KSA). The time series of NDVIwas utilized to assess the vegetation pattern change in the study area. The currentstudy area has sparse vegetation, and exposed soil exhibits brightness due to lowsoil moisture, constraining NDVI. Therefore, a machine learning (ML) basedRandom Forest (RF) classification model was used to compare the vegetationextent and computational cost of NDVI. The RF model has been compared withNDVI in the current investigation. It is one of the most precise classificationmethods because it can model the complexity of input variables, handle outliers,treat noise effectively, and avoid overfitting. Multinomial Logistic Regression(MLR) was implemented to compare the performance of both NDVI and RFbased classification. RF model provided good accuracy (98%) for all vegetationclasses based on user accuracy, producer accuracy, and kappa coefficient.