Comparison of spectral categorisation of different Sentinel-2 bottom-of-atmosphere products

Suggested by: Martin Sudmanns, Thomas Strasser, Dirk Tiede

Short description: Satellite Earth observations (EO) are measurements: They need to be calibrated very well in order to provide high-quality information. One calibration step is the calculation of bottom-of-atmosphere reflectance values with the aim to remove or reduce atmospheric influences. Several different approaches and technical implementation exist in parallel (e.g. Sen2Cor, FORCE, ..)

The satellite image automated mapper (SIAM) provides spectral categories from reflectance values. Using a per-pixel physical spectral model-based decision tree, SIAM automatically categorises EO imagery based on reflectance values from multiple optical sensors (e.g., Sentinel-2, Landsat-8, AVHRR, VHR) The software is capable of producing different granularities (i.e. different number of colour names) from coarse (i.e., 18 colour names) to fine (i.e., 96 colour names), as well as additional data-derived information layers (e.g., multi-spectral greenness index, brightness). While SIAM-based categories are routinely derived from Sentinel-2 top-of-atmosphere reflectance values, they have not yet been used on bottom-of-atmosphere calibrated reflectance values.

The goal of this master thesis is to investigate and compare the spectral categories from SIAM on different bottom-of-atmosphere calibrated Sentinel-2 images.

References, suggested reading:

• Baraldi, A.; Durieux, L.; Simonetti, D.; Conchedda, G.; Holecz, F.; Blonda, P. Automatic Spectral-Rule-Based Preliminary Classification of Radiometrically Calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 Imagery—Part I: System Design and Implementation. IEEE Trans. Geosci. Remote Sens. 2010, 48, 1299–1325.

• Rumora, L.; Miler, M.; Medak, D. Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers. ISPRS Int. J. Geo-Inf. 2020, 9, 277. https://doi.org/10.3390/ijgi9040277

Related to projects: https://sen2cube.at 

Start/finish: anytime

Prerequisites/qualifications: 

    + Remote Sensing

    + Programming