Filippo Milazzo’s Post

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PhD Project Manager-researcher

🌍 Addressing Geometric Challenges in Satellite Imaging for Land Monitoring 🛰 Satellite images often suffer from geometric errors that impair land monitoring and change detection analysis. These misalignments between consecutive acquisitions introduce noise and inaccuracies, particularly in high-temporal-resolution data collections such as Sentinel-2, Landsat, and PlanetScope. The article, written by Peresutti explores how to co-register a temporal stack of optical satellite images to mitigate these errors. Through extensive experiments, a workflow was developed that uses image-based co-registration to accurately align temporal images, thereby improving analysis accuracy. It was found that using an average temporal image as the template and a translation-only motion model produces the best results, significantly reducing the impact of geometric errors. These findings have been incorporated into the eo-learn library to facilitate use by other researchers and professionals. 🔗 More details and results are available in the blog: https://lnkd.in/dMfuqw3g #SatelliteImagery #GeometricErrors #LandMonitoring #ChangeDetection #MachineLearning #Sentinel2 #RemoteSensing #eoLearn #GIS #DataScience

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