S
Saeid Homayouni
Researcher at Institut national de la recherche scientifique
Publications - 176
Citations - 2952
Saeid Homayouni is an academic researcher from Institut national de la recherche scientifique. The author has contributed to research in topics: Hyperspectral imaging & Support vector machine. The author has an hindex of 20, co-authored 142 publications receiving 1726 citations. Previous affiliations of Saeid Homayouni include University of Tehran & Ottawa University.
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Journal ArticleDOI
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
Mohammadreza Sheykhmousa,Masoud Mahdianpari,Hamid Ghanbari,Fariba Mohammadimanesh,Pedram Ghamisi,Saeid Homayouni +5 more
TL;DR: A meta-analysis of 251 peer-reviewed journal papers relevant to remote sensing image classification and a comparative analysis regarding the performances of RF and SVM classification against various parameters is applied.
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The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms
Heather McNairn,Thomas J. Jackson,Grant Wiseman,Stéphane Bélair,Aaron A. Berg,Paul R. Bullock,Andreas Colliander,Michael H. Cosh,Seung-Bum Kim,Ramata Magagi,Mahta Moghaddam,Eni G. Njoku,Justin R. Adams,Saeid Homayouni,Emmanuel Ojo,Tracy Rowlandson,Jiali Shang,Kalifa Goïta,Mehdi Hosseini +18 more
TL;DR: Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach.
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The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform
TL;DR: This study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent and suggests a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large- scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.”
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RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring
TL;DR: This study demonstrates that polarimetric SAR responds to accumulation of dry biomass, but as well that several radar parameters can uniquely identify changes in crop structure and phenology.
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Anomaly Detection in Hyperspectral Images Based on an Adaptive Support Vector Method
TL;DR: An attempt to address the main problem using the Gaussian kernel-based AD methods is the optimal setting of sigma, with a direct and adaptive measure based on a geometric interpretation of the GK-SVDD.