Abstract
Abstract. The requirement for very high-resolution satellite imagery by different applications has been increasing continuously. Several commercial and government-supported missions provide sub-meter spatial resolutions from optical sensors aboard Earth Observation (EO) satellites. The MAXAR satellite constellation acquires images with up to 30 cm Ground Sampling Distances (GSDs); and the High-Definition (HD) image production technology developed by MAXAR doubles the resolution by using artificial intelligence methods. Although the spatial resolution is one of the most important image quality metrics, several other factors indicated by diverse radiometric and geometric characteristics may circumscribe the usability of data in different projects. As part of mandatory activities of European Space Agency (ESA), Earthnet Programme provides a framework for integrating Third-Party Missions into the overall EO strategy and promotes the international use of the data. The Earthnet Data Assessment Pilot (EDAP) project aims at assessing the quality and the suitability of TPMs, and provides a communication platform between mission providers to ensure the coherence of the systems. In this study, the radiometric quality of the MAXAR HD products was evaluated within the EDAP project framework by using several General Image-Quality Equation (GIQE) metrics, visual inspections, and comparative assessments with orthophotos obtained from an Unmanned Aerial Vehicle (UAV) platform and with the original (non-HD) orthophotos with 30 cm resolutions. The results show that the spatial resolution improvements are observable in urban areas, where sharp edges are present. However, blurring and color noise patterns also occured in the HD images.
Highlights
The variety of applications using satellite optical imagery with very high spatial resolution at sub-meter level is increasing each day, such as agriculture (Zhang et al, 2020), land use land cover (LULC) classification (Zhang et al, 2018), 3D surface and building modeling (Poli et al, 2015), ecosystem modeling (Gruen et al, 2017), etc
The correction deals with artefacts that are not sourced from image objects or scene; instead sensor errors, i.e. non-responsive detectors, scanner inconsistencies, and atmospheric interference (DigitalGlobe, 2014)
The Unmanned Aerial Vehicle (UAV)-MAXAR HD data were compared visually by using the pan HD data of WV-3 and the green band of the UAV orthophotos downsampled to 15 cm resolution
Summary
The variety of applications using satellite optical imagery with very high spatial resolution at sub-meter level is increasing each day, such as agriculture (Zhang et al, 2020), land use land cover (LULC) classification (Zhang et al, 2018), 3D surface and building modeling (Poli et al, 2015), ecosystem modeling (Gruen et al, 2017), etc. As part of radiometric quality improvement efforts, satellite image vendors such as MAXAR Technologies aim to generate image products with even higher spatial resolutions by applying machine learning algorithms (Blog MAXAR, 2021). The High-Definition (HD) image production technology developed by MAXAR increases the image GSD to 15 cm by using artificial intelligence methods (Blog MAXAR, 2021). The HD products are output of an image processing algorithm, and questions can be raised regarding the radiometry, and how the image content is preserved and/or transformed
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