Robotics & Machine Learning Daily News2024,Issue(Sep.24) :80-80.

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)

Robotics & Machine Learning Daily News2024,Issue(Sep.24) :80-80.

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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Abstract

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.”

Key words

Porto Alegre/Brazil/South America/Mac hine Learning/Support Vector Machines/Federal University Rio Grande do Sul

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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