首页|Researchers from Wuhan University Discuss Research in Machine Learning (Comparis on of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Fea tures for Mapping Urban Impervious Surface)
Researchers from Wuhan University Discuss Research in Machine Learning (Comparis on of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Fea tures for Mapping Urban Impervious Surface)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news originating from Wuhan, People 's Republic of China, by NewsRx correspondents, research stated, "The integratio n of optical and SAR datasets through ensemble machine learning models shows pro mising results in urban remote sensing applications. The integration of multi-se nsor datasets enhances the accuracy of information extraction." Funders for this research include National Key Research And Development Program of China; Guangxi Science And Technology Program Guangxi Key R&D Pl an; Sichuan Science And Technology Program; Hubei Key R&D Plan. The news editors obtained a quote from the research from Wuhan University: "This research presents a comparison of two ensemble machine learning classifiers (ra ndom forest and extreme gradient boost (XGBoost)) classifiers using an integrati on of optical and SAR features and simple layer stacking (SLS) techniques. There fore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textu res and enhanced modified indices to extract features for the year 2023. The cla ssification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three sig nificant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seo ul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an opt ical feature. Results showed an overall accuracy of 81% for UIS cl assification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperf ormed the RF algorithm and Dynamic World (DW) data product and comparatively sho wed higher classification accuracy."
Wuhan UniversityWuhanPeople's Republ ic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRemote Sens ing
NETL
NSTL
