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Semi-supervised learning for forest fire segmentation using UAV imagery
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Wang, Junling, Fan, Xijian, Yang, Xubing, Tjahjadi, Tardi and Wang, Yupeng (2022) Semi-supervised learning for forest fire segmentation using UAV imagery. Forests, 13 (10). 1573. doi:10.3390/f13101573
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Official URL: https://doi.org/10.3390/f13101573 (registering DOI...
Abstract
Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location of a forest fire. However, training a semantic segmentation model requires a large number of labeled images, which is labor-intensive and time-consuming to generate. To address the lack of labeled images, we propose, in this paper, a semi-supervised learning-based segmentation network, SemiFSNet. By taking into account the unique characteristics of UAV-acquired imagery of forest fire, the proposed method first uses occlusion-aware data augmentation for labeled data to increase the robustness of the trained model. In SemiFSNet, a dynamic encoder network replaces the ordinary convolution with dynamic convolution, thus enabling the learned feature to better represent the fire feature with varying size and shape. To mitigate the impact of complex scene background, we also propose a feature refinement module by integrating an attention mechanism to highlight the salient feature information, thus improving the performance of the segmentation network. Additionally, consistency regularization is introduced to exploit the rich information that unlabeled data contain, thus aiding the semi-supervised learning. To validate the effectiveness of the proposed method, extensive experiments were conducted on the Flame dataset and Corsican dataset. The experimental results show that the proposed model outperforms state-of-the-art methods and is competitive to its fully supervised learning counterpart.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > Q Science (General) S Agriculture > SD Forestry T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Forest fires, Forest fires -- Detection , Forest fires -- Prevention and control, Drone aircraft, Supervised learning (Machine learning) , Semantic Web, Image segmentation , Neural networks (Computer science) | |||||||||||||||
| Journal or Publication Title: | Forests | |||||||||||||||
| Publisher: | MDPI | |||||||||||||||
| ISSN: | 1999-4907 | |||||||||||||||
| Official Date: | 26 September 2022 | |||||||||||||||
| Dates: |
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| Volume: | 13 | |||||||||||||||
| Number: | 10 | |||||||||||||||
| Article Number: | 1573 | |||||||||||||||
| DOI: | 10.3390/f13101573 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Description: | (This article belongs to the Special Issue Deep Learning Techniques for Forest Parameter Retrieval and Accurate Tree Modeling from Remote Sensing Data) |
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