Abstract
Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task. However, for most applications, many of the images in the archive are redundant and do not contribute to the quality of the final result. An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing, while retaining the quality of the resulting output. As a case study, we focused on the creation of a cloud-free composite, covering the global land mass and based on all the images acquired from January 2016 until September 2017. The total amount of available images was 2,128,556. The selection of the optimal subset was based on quicklooks, which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed. The selected subset contained 94,093 image tiles in total, reducing the amount of images to be processed to 4.42% of the full set.
Highlights
The Copernicus program of the European Commission (EC) with its Sentinel satellites produces approximately 10 TB of Earth Observation (EO) data per day
An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing, while retaining the quality of the resulting output
The selection of the optimal subset was based on quicklooks, which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed
Summary
The Copernicus program of the European Commission (EC) with its Sentinel satellites produces approximately 10 TB of Earth Observation (EO) data per day. This wealth of information, combined with a free full and open access policy, provides new opportunities for applications in forestry, agriculture, and climate change monitoring, to name a few. Instead of dealing with the original images at full resolution, the selection criterion is based on a sample only. The underlying idea is that the computational cost of the optimization function is considerably less than the processing algorithm that is needed to produce the application results.
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