Unsupervised Domain Adaptation for Forest Fire Recognition Using Transferable Knowledge from Public Datasets

Unsupervised Domain Adaptation for Forest Fire Recognition Using Transferable Knowledge from Public Datasets

Deep neural networks (DNNs) have driven the recent advances in fire detection. However, existing methods require large-scale labeled samples to train data-hungry networks, which are difficult to collect and even more laborious to label. This paper applies unsupervised domain adaptation (UDA) to tran...

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Bibliographic Details
Main Authors: Zhengjun Yan, Liming Wang, Kui Qin, Feng Zhou, Jineng Ouyang, Teng Wang, Xinguo Hou, Leping Bu
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Forests
Subjects:
forest fire recognition
unsupervised domain adaptation
transfer learning
deep neural network
Online Access:https://www.mdpi.com/1999-4907/14/1/52

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https://www.mdpi.com/1999-4907/14/1/52

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