Fire images classification based on a handcraft approach

作者：

Highlights：

• The current study extracts higher-order features from fire images.

• A new set of data is correctly labelled for classifying fire and non-fire.

• Information-theoretic feature selection is adopted to minimize computational cost.

• The SVM performs classification with an RBF kernel.

• The model draws an overall accuracy of 96,21%, and a specificity of 97,99%.

• The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively.

摘要

•The current study extracts higher-order features from fire images.•A new set of data is correctly labelled for classifying fire and non-fire.•Information-theoretic feature selection is adopted to minimize computational cost.•The SVM performs classification with an RBF kernel.•The model draws an overall accuracy of 96,21%, and a specificity of 97,99%.•The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively.

论文关键词：Wildfire,Higher-order features,Feature selection,Support Vector Machine (SVM),Radial Basis Function (RBF)

论文评审过程：Received 14 March 2022, Revised 14 August 2022, Accepted 14 August 2022, Available online 23 August 2022, Version of Record 6 September 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118594