首页|University of the Philippines Diliman Researchers Advance Knowledge in Machine L earning (Integration of Stumpf’s Ratio Model And Random Forest For Satellite-der ived Bathymetry Estimation)
University of the Philippines Diliman Researchers Advance Knowledge in Machine L earning (Integration of Stumpf’s Ratio Model And Random Forest For Satellite-der ived Bathymetry Estimation)
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 reporting out of the Universit y of the Philippines Diliman by NewsRx editors, research stated, “The developmen t of remote sensing for coastal and marine environment mapping has significantly enhanced our understanding of these ecosystems, enabling improved mitigation st rategies against the impacts of human activities.” Our news reporters obtained a quote from the research from University of the Phi lippines Diliman: “However, remote sensing must consider the complex interplay o f the atmosphere and water column. Ongoing research focuses on refining water co lumn correction techniques, including Depth Invariant Indices (DII), Radiative T ransfer models, and bathymetry models. This study specifically aims to enhance t he Stumpf’s Ratio model (SRM) for bathymetry by employing the Random Forest (RF) machine learning regression algorithm. The resulting bathymetry model, which in corporates visible bands from a Sentinel-2 MSI image, and their Stumpf’s ratios, outperforms other methods, yielding the highest accuracy with RMSE and R2 values of 1.25 m and 0.854, respectively.”
University of the Philippines Diliman, C yborgs, Emerging Technologies, Machine Learning, Remote Sensing
NETL
NSTL
