首页|New Support Vector Machines Findings from Federal University Rio Grande do Sul D escribed (Classification of Semideciduous Seasonal Forest Successional Stages Us ing Sentinel-1-2 and Srtm Data On Google Earth Engine)
New Support Vector Machines Findings from Federal University Rio Grande do Sul D escribed (Classification of Semideciduous Seasonal Forest Successional Stages Us ing Sentinel-1-2 and Srtm Data On Google Earth Engine)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning - Su pport Vector Machines have been presented. According to news reporting from Port o Alegre, Brazil, by NewsRx journalists, research stated, “Remote sensing data u sed in this study included MSI (Multispectral Instrument) Sentinel -2, SAR (Synt hetic Aperture Radar) Sentinel -1, GLCM (Grey Level Co -Occurrence Matrix) textu re data derived from Sentinel -1, and geomorphometric data derived from SRTM (Sh uttle Radar Topography Mission) images. The input data was divided into separate groups for machine learning algorithms, including Support Vector Machine (SVM), Classification and Regression Tree (CART), and Random Forest (RF), which were i mplemented on the Google Earth Engine platform.”
Porto AlegreBrazilSouth AmericaMac hine LearningSupport Vector MachinesFederal University Rio Grande do Sul