Abstract
Abstract. With the increasing availability of satellite imagery at several spatial, spectral and temporal resolutions, the choice of the best image and the most appropriate method for object detection and classification of a broad range of land surface classes or processes is still a difficult task for many users. In order to guide the users, we proposed a user-tailored machine learning method (IMage CLASSification - ImCLASS) to detect and classifiy specific landcover classes.The method assumes a mono-class approach taking several ill-posed problems (e.g. class imbalance, high diversity inside the studied class, similarities with the adjacent samples…) as use cases (landslides, construction works in urban areas, burnt areas, vegetation classes…). It is a generalization of the ALADIM processor already validated in the context of landslide mapping and available as a service on the ESA GeoHazards Exploitation Platform (GEP). The proposed chain is able to combine optical and radar images, uses open source libraries, and is optimized for rapid calculation on HPC environments. The ImCLASS processor is presented and its performance is evaluated on three use cases: landslide detection and mapping after disasters in different regions of the World, urban classes change detection with a focus on construction works in Strasbourg, and crop mapping (vineyard) in the Grand-Est region. First results using either bi-dates or mono-date imagery are presented.
Highlights
In many scientific domains, an increased emphasis is currently observed for data mining techniques to extract information from large remote sensing datasets (Navalgund et al, 2007; Lu and Weng, 2007)
ImCLASS builds on the previous ALADIM image classification system available as a service on the ESA GeoHazards Exploitation Platform (GEP)
In order to facilitate the porting on calculation clusters, ImCLASS is coded in Python 3.6, embedded in Docker/Singularity environments. and deployed on the HPC hardware of A2S hosted at the Datacenter of University of Strasbourg
Summary
An increased emphasis is currently observed for data mining techniques to extract information from large remote sensing datasets (Navalgund et al, 2007; Lu and Weng, 2007). Since 2018, A2S 'Application Satellite Survey' initiated the development of the ImCLASS change detection and classification processor. ImCLASS targets the supervised analysis of optical and SAR remote sensing images and the use of a machine learning approach including features extraction, feature dimension reduction and feature classification. ImCLASS builds on the previous ALADIM image classification system (developed at EOST and LIVE; Stumpf et al, 2014) available as a service on the ESA GeoHazards Exploitation Platform (GEP). The objective of this manuscript is first to briefly present the processing chain, and second, to evalute its performance for three uses cases
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