Wildfires identification: Semantic segmentation using support vector machine classifier

Pecha, Marek, et al. "Wildfires identification: Semantic segmentation using support vector machine classifier." Programs and Algorithms of Numerical Mathematics. Prague: Institute of Mathematics CAS, 2023. 173-186. <http://eudml.org/doc/299024>.

@inProceedings{Pecha2023,
abstract = {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.},
author = {Pecha, Marek, Langford, Zachary, Horák, David, Tran Mills, Richard},
booktitle = {Programs and Algorithms of Numerical Mathematics},
keywords = {wildfire identification; semantic segmentation; support vector machines; distributed training},
location = {Prague},
pages = {173-186},
publisher = {Institute of Mathematics CAS},
title = {Wildfires identification: Semantic segmentation using support vector machine classifier},
url = {http://eudml.org/doc/299024},
year = {2023},
}

TY - CLSWK
AU - Pecha, Marek
AU - Langford, Zachary
AU - Horák, David
AU - Tran Mills, Richard
TI - Wildfires identification: Semantic segmentation using support vector machine classifier
T2 - Programs and Algorithms of Numerical Mathematics
PY - 2023
CY - Prague
PB - Institute of Mathematics CAS
SP - 173
EP - 186
AB - 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.
KW - wildfire identification; semantic segmentation; support vector machines; distributed training
UR - http://eudml.org/doc/299024
ER -