{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:48:20Z","timestamp":1784648900077,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971668"],"award-info":[{"award-number":["31971668"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest fires have become a significant global threat, with many negative impacts on human habitats and forest ecosystems. This study proposed a forest fire identification method by fusing visual and infrared images, addressing the high false alarm and missed alarm rates of forest fire monitoring using single spectral imagery. A dataset suitable for image fusion was created using UAV aerial photography. An improved image fusion network model, the FF-Net, incorporating an attention mechanism, was proposed. The YOLOv5 network was used for target detection, and the results showed that using fused images achieved a higher accuracy, with a false alarm rate of 0.49% and a missed alarm rate of 0.21%. As such, using fused images has greater significance for the early warning of forest fires.<\/jats:p>","DOI":"10.3390\/rs15123173","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"3173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Forest Fire Monitoring Method Based on UAV Visual and Infrared Image Fusion"],"prefix":"10.3390","volume":"15","author":[{"given":"Yuqi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6187-2967","authenticated-orcid":false,"given":"Change","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Cui","sequence":"additional","affiliation":[{"name":"Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry, Sault St. Marie, ON 279541, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41561-021-00763-8","article-title":"Fire enhances forest degradation within forest edge zones in Africa","volume":"14","author":"Zhao","year":"2021","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Benzekri, W., El Moussati, A., Moussaoui, O., and Berrajaa, M. (2020). Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning. Int. J. Adv. Comput. Sci. Appl., 11.","DOI":"10.14569\/IJACSA.2020.0110564"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1038\/s41467-021-26838-z","article-title":"Forest fires and climate-induced tree range shifts in the western US","volume":"12","author":"Hill","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Salavati, G., Saniei, E., Ghaderpour, E., and Hassan, Q.K. (2022). Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models. Sustainability, 14.","DOI":"10.3390\/su14073881"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ghorbani, K., Baum, T.C., and Thompson, L. (November, January 29). Properties and Radar Cross-Section of forest fire ash particles at millimeter wave. Proceedings of the Microwave Conference (EuMC), 2012 42nd European, Amsterdam, The Netherlands.","DOI":"10.23919\/EuMC.2012.6459098"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1742-6596\/1792\/1\/012022","article-title":"Forest Fire Compound Feature Monitoring Technology Based on Infrared and Visible Binocular Vision","volume":"1792","author":"Sun","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., and Cui, W. (2021). Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14010045"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012068","DOI":"10.1088\/1742-6596\/1982\/1\/012068","article-title":"Forest Wildfire Monitoring and Communication UAV System Based on Particle Swarm Optimization","volume":"1982","author":"Yang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_9","unstructured":"Kizilkaya, B., Ever, E., Yekta, Y.H., and Yazici, A. (2022). ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Association for Computing Machinery."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Y., Xin, J., Yi, Y., Liu, D., and Liu, H. (2018, January 25\u201327). A UAV-based Forest Fire Detection Algorithm Using Convolutional Neural Network. Proceedings of the 2018 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8484035"},{"key":"ref_11","unstructured":"Chi, Y., Liu, Z., and Zhang, Y. (2015, January 9\u201312). UAV-based forest fire detection and tracking using image processing techniques. Proceedings of the International Conference on Unmanned Aircraft Systems, Denver, CO, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2203\/1\/012013","article-title":"Wheat Canopy Cover Estimation by Optimized Random Forest and UAV Multispectral imagery","volume":"2203","author":"Zhang","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Zhang, Y., Mu, L., Xin, J., Jiao, S., Liu, H., and Liu, D. (2020, January 22\u201324). A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire Detection. Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9163816"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106574","DOI":"10.1016\/j.ast.2021.106574","article-title":"Infra-red line camera data-driven edge detector in UAV forest fire monitoring","volume":"111","author":"Fdv","year":"2021","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_15","first-page":"7","article-title":"Research on real-time forest fire spread prediction model based on UAV","volume":"50","author":"Wang","year":"2022","journal-title":"For. Mach. Woodwork. Equip."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2341010","DOI":"10.1142\/S1469026823410109","article-title":"An Efficiency Correlation between Various Image Fusion Techniques","volume":"22","author":"Nayagi","year":"2023","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4070","DOI":"10.1109\/TIP.2021.3069339","article-title":"Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4412","DOI":"10.1049\/iet-ipr.2020.1165","article-title":"Two-scale fusion method of infrared and visible images via parallel saliency features","volume":"14","author":"Duan","year":"2021","journal-title":"IET Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104041","DOI":"10.1016\/j.infrared.2022.104041","article-title":"Significant target analysis and detail preserving based infrared and visible image fusion","volume":"121","author":"Yin","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_20","unstructured":"Alexander, T. (2023, June 16). TNO Image Fusion Dataset. Available online: https:\/\/www.altmetric.com\/details\/2309122."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Prabhakar, K.R., Srikar, V.S., and Babu, R.V. (2017, January 22\u201329). DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.505"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TCI.2020.2965304","article-title":"VIF-Net: An Unsupervised Framework for Infrared and Visible Image Fusion","volume":"6","author":"Hou","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_24","unstructured":"Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). BAM: Bottleneck Attention Module. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_26","first-page":"502","article-title":"U2Fusion: A Unified Unsupervised Image Fusion Network","volume":"26","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","first-page":"2614","article-title":"DenseFuse: A Fusion Approach to Infrared and Visible Images","volume":"28","author":"Hui","year":"2018","journal-title":"IEEE Trans. Image Process"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_29","first-page":"012001","article-title":"A smoking behavior detection method based on the YOLOv5 network","volume":"2232","author":"Jiang","year":"2022","journal-title":"J. Physics: Conf. Ser."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhou, H., Li, Z., Gao, Y., Bai, D., Xu, R., and Lin, H. (2023). Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s. Forests, 14.","DOI":"10.3390\/f14020315"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3173\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:59Z","timestamp":1760126159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3173"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,18]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123173"],"URL":"https:\/\/doi.org\/10.3390\/rs15123173","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,18]]}}}