首页|Study Results from University of Cadi Ayyad Provide New Insights into Machine Le arning (Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identifica tion ...)
Study Results from University of Cadi Ayyad Provide New Insights into Machine Le arning (Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identifica tion ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of the University of Cadi Ayyad by NewsRx editors, research stated, “Forest resource conservation necessi tates a deeper understanding of forest ecosystem processes and how future manage ment decisions and climate change may affect these processes. Argania spinosa (L .) Skeels is one of the most popular species in Morocco.” Our news correspondents obtained a quote from the research from University of Ca di Ayyad: “Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offe rs a powerful resource for mapping, assessing, and monitoring the forest tree sp ecies at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Syntheti c Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) o ver the Argan forest in Essaouira province, Morocco, were subjected to pixel-bas ed machine learning classification and analysis. We investigated the influence o f different SAR data parameters and DEM layers on the performance of machine lea rning algorithms. In addition, we evaluated the synergistic effects of integrati ng remote sensing data, including optical, SAR, and DEM data, for identifying ar gan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRT M DEM, and ground truth sources to achieve our goal. Testing different SAR param eters and integrating DEM layers of different resolutions with other remote sens ing data showed that the Lee Sigma filter with a size of 11 x 11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algor ithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the comb ined use of two Sentinel constellation products improved map accuracy and qualit y.”
University of Cadi AyyadCyborgsEmerg ing TechnologiesMachine LearningRemote Sensing
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