Název:
Semantic segmentation using support vector machine classifier
Autoři:
Pecha, Marek ; Langford, Z. ; Horák, David ; Tran Mills, R. Typ dokumentu: Příspěvky z konference Konference/Akce: Programs and Algorithms of Numerical Mathematics /21./, Jablonec nad Nisou (CZ), 20220619
Rok:
2023
Jazyk:
eng
Abstrakt: This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with $\mathcal{l}1$-loss and $\mathcal{l}2$-loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.
Klíčová slova:
distributed training; semantic segmentation; support vector machines; wildfire identification Číslo projektu: 847593 Zdrojový dokument: Programs and Algorithms of Numerical Mathematics 21 : Proceedings of Seminar, ISBN 978-80-85823-73-8