Open AccessFusion of Radar and Optical Data for Land Cover Classification Using Machine Learning Approach
Author(s) -
David Nhemaphuki,
Kiran Thapa Chetri,
Sanjeevan Shrestha
Publication year - 2020
Publication title -
nepalese journal of geoinformatics/journal of geoinformatics nepal
Language(s) - English
Resource type - Journals
eISSN - 2717-5022
pISSN - 2676-1246
DOI - 10.3126/njg.v20i1.39476
Subject(s) - land cover , radar , remote sensing , random forest , cover (algebra) , computer science , 3d optical data storage , radar imaging , fusion , sensor fusion , contextual image classification , land use , artificial intelligence , geography , image (mathematics) , engineering , telecommunications , linguistics , civil engineering , philosophy , operating system , mechanical engineering
This study evaluates the advantages of combining traditional space borne optical data with longer wavelengths of radar for land cover mapping. Land cover classification was carried out using Optical, radar data and combination of both for the Bardiya district using Random Forest algorithm. The fusion of optical and radar shows better land cover discrimination with 96.98% overall accuracy in compared to using radar data and optical data separately with overall accuracy of 69.2% and 95.89% respectively. Additionally, the qualitative result demonstrates that the combined utilization of optical and radar imagery yields useful land cover information over those obtained using either type of image on its own.