文献类型：期刊文献

英文题名：Weakly Supervised Forest Fire Segmentation in UAV Imagery Based on Foreground-Aware Pooling and Context-Aware Loss

作者：Wang, Junling[1,2] Wang, Yupeng[3] Liu, Liping[2] Yin, Hengfu[2] Ye, Ning[1] Xu, Can[3]

第一作者：Wang, Junling

通信作者：Xu, C[1]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, State Key Lab Tree Genet & Breeding, Hangzhou 311400, Peoples R China;[3]Nanjing Univ Sci & Technol, Coll Comp Sci & Engn, Nanjing 210094, Peoples R China

年份：2023

卷号：15

期号：14

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20233114470591);Scopus(收录号：2-s2.0-85166259887);WOS:【SCI-EXPANDED(收录号:WOS:001038877500001)】；

基金：This research was funded by Nonprofit Research Projects (CAFYBB2021QD001-1) of Chinese Academy of Forestry and the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding (2021C02070-1) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_1105).

语种：英文

外文关键词：forest fire; UAV imagery; intelligent forestry; weakly supervised learning; semantic segmentation

摘要：In recent years, tragedies caused by forest fires have been frequently reported. Forest fires not only result in significant economic losses but also cause environmental damage. The utilization of computer vision techniques and unmanned aerial vehicles (UAVs) for forest fire monitoring has become a primary approach to accurately locate and extinguish fires during their early stages. However, traditional computer-based methods for UAV forest fire image segmentation require a large amount of pixel-level labeled data to train the networks, which can be time-consuming and costly to acquire. To address this challenge, we propose a novel weakly supervised approach for semantic segmentation of fire images in this study. Our method utilizes self-supervised attention foreground-aware pooling (SAP) and context-aware loss (CAL) to generate high-quality pseudo-labels, serving as substitutes for manual annotation. SAP collaborates with bounding box and class activation mapping (CAM) to generate a background attention map, which aids in the generation of accurate pseudo-labels. CAL further improves the quality of the pseudo-labels by incorporating contextual information related to the target objects, effectively reducing environmental noise. We conducted experiments on two publicly available UAV forest fire datasets: the Corsican dataset and the Flame dataset. Our proposed method achieved impressive results, with IoU values of 81.23% and 76.43% for the Corsican dataset and the Flame dataset, respectively. These results significantly outperform the latest weakly supervised semantic segmentation (WSSS) networks on forest fire datasets.