| Title |
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets / |
| Authors |
Šidlauskas, Arminas ; Kriščiūnas, Andrius |
| Full Text |
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| Is Part of |
CEUR workshop proceedings: proceedings of the 27th international conference on information society and university studies (IVUS 2022), Kaunas, Lithuania, May 12, 2022 / edited by: T. Krilavičius, A. Lopata, I. Veitaitė, M. Woźniak, Ch. Napoli, D. Kalinauskaitė.. Aachen : CEUR-WS. 2022, vol. 3611, p. 39-45.. ISSN 1613-0073 |
| Keywords [eng] |
forest classification ; Sentinel-2 imagery ; fully convolutional network ; copernicus high resolution layers ; openstreetmap |
| Abstract [eng] |
Remote sensing tools are becoming popular in gathering information about forest area changes. The European Space Agency has launched multiple Sentinel satellites for land and marine monitoring. The Sentinel-2 (S2) satellite has great forest monitoring capabilities with its 13 high resolution bands. With the capabilities provided by this satellite, high accuracy pixel-based classification can be applied. In order to train a model that would be well suited to recognize forested areas from S2 images, a solid training dataset must be provided. In this study, two different information sources, Copernicus High Resolution Layers (HRL) and OpenStreetMap (OSM), were used to automatically create datasets. Models were trained and evaluated using the same artificial neural network architecture. After further analysis, it was noted that both OSM and HRL trained models yielded similar numerical evaluation results. Both models adjusted well to their data source classification and reached similar evaluation results of around 0.92 pixel accuracy. Upon further visual inspection, it was noted that OSM trained models created more false negative classifications identifying small forest patches and forest areas along rivers/lakes, HRL on the other hand created more false positives when identifying not only areas along rivers but rivers themselves as forest. All models failed to properly identify forest clearings in large forest areas, although HRL-trained models provided slightly better results. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2022 |
| CC license |
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