{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:03:53Z","timestamp":1760058233212,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology Major Project","award":["AA19254016","Bei Kehe 2023158004"],"award-info":[{"award-number":["AA19254016","Bei Kehe 2023158004"]}]},{"name":"Beihai Science and Technology Bureau Project","award":["AA19254016","Bei Kehe 2023158004"],"award-info":[{"award-number":["AA19254016","Bei Kehe 2023158004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Recognizing mangrove species is a challenging task in coastal wetland ecological monitoring due to the complex environment, high species similarity, and the inherent symmetry within the structural features of mangrove species. Many species coexist, exhibiting only subtle differences in leaf shape and color, which increases the risk of misclassification. Additionally, mangroves grow in intertidal environments with varying light conditions and surface reflections, further complicating feature extraction. Small species are particularly hard to distinguish in dense vegetation due to their symmetrical features that are difficult to differentiate at the pixel level. While hyperspectral imaging offers some advantages in species recognition, its high equipment costs and data acquisition complexity limit its practical application. To address these challenges, we propose MHAGFNet, a segmentation-based mangrove species recognition network. The network utilizes easily accessible RGB remote sensing images captured by drones, ensuring efficient data collection. MHAGFNet integrates a Multi-Scale Feature Fusion Module (MSFFM) and a Multi-Head Attention Guide Module (MHAGM), which enhance species recognition by improving feature capture across scales and integrating both global and local details. In this study, we also introduce MSIDBG, a dataset created using high-resolution UAV images from the Shankou Mangrove National Nature Reserve in Beihai, China. Extensive experiments demonstrate that MHAGFNet significantly improves accuracy and robustness in mangrove species recognition.<\/jats:p>","DOI":"10.3390\/sym17030461","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T07:48:37Z","timestamp":1742370517000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient Coastal Mangrove Species Recognition Using Multi-Scale Features Enhanced by Multi-Head Attention"],"prefix":"10.3390","volume":"17","author":[{"given":"Shaolin","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Guilin University of Electronic Technology, Beihai 536000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-5623","authenticated-orcid":false,"given":"Tonglai","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1743-3139","authenticated-orcid":false,"given":"Qin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Guilin Universityof Electronic Technology, Beihai 536000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105094","DOI":"10.1016\/j.marpol.2022.105094","article-title":"Spatial multi-criteria analysis to capture socio-economic factors in mangrove conservation","volume":"141","author":"Trialfhianty","year":"2022","journal-title":"Mar. Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5050","DOI":"10.1038\/s41467-021-25349-1","article-title":"A meta-analysis of the ecological and economic outcomes of mangrove restoration","volume":"12","author":"Su","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106758","DOI":"10.1016\/j.ecolecon.2020.106758","article-title":"Costs and carbon benefits of mangrove conservation and restoration: A global analysis","volume":"176","author":"Jakovac","year":"2020","journal-title":"Ecol. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1016\/j.cub.2021.01.070","article-title":"Global potential and limits of mangrove blue carbon for climate change mitigation","volume":"31","author":"Zeng","year":"2021","journal-title":"Curr. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1038\/s41467-023-36477-1","article-title":"Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change","volume":"14","author":"Song","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"104747","DOI":"10.1016\/j.marpol.2021.104747","article-title":"Valuing habitat quality for managing mangrove ecosystem services in coastal Tangerang District, Indonesia","volume":"133","author":"Marlianingrum","year":"2021","journal-title":"Mar. Policy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10456","DOI":"10.1073\/pnas.0804601105","article-title":"Mangroves in the Gulf of California increase fishery yields","volume":"105","author":"Ezcurra","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1038\/s41467-023-44321-9","article-title":"More than 17,000 tree species are at risk from rapid global change","volume":"15","author":"Boonman","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1007\/s40747-021-00457-z","article-title":"Remote sensing techniques: Mapping and monitoring of mangrove ecosystem\u2014A review","volume":"7","author":"Maurya","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tomlinson, P.B. (2016). The Botany of Mangroves, Cambridge University Press.","DOI":"10.1017\/CBO9781139946575"},{"key":"ref_11","first-page":"102890","article-title":"Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images","volume":"112","author":"Fu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1080\/10106049.2018.1520923","article-title":"Mapping distribution of Sundarban mangroves using Sentinel-2 data and new spectral metric for detecting their health condition","volume":"35","author":"Manna","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Setyadi, G., Pribadi, R., Wijayanti, D.P., and Sugianto, D.N. (2021). Mangrove diversity and community structure of Mimika District, Papua, Indonesia. Biodivers. J. Biol. Divers., 22.","DOI":"10.13057\/biodiv\/d220857"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108148","DOI":"10.1016\/j.ecolind.2021.108148","article-title":"Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects","volume":"130","author":"Zulfa","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, X., Tan, L., and Fan, J. (2023). Performance evaluation of mangrove species classification based on multi-source Remote Sensing data using extremely randomized trees in Fucheng Town, Leizhou city, Guangdong Province. Remote Sens., 15.","DOI":"10.3390\/rs15051386"},{"key":"ref_17","first-page":"102414","article-title":"Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm","volume":"102","author":"Cao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"0146","DOI":"10.34133\/remotesensing.0146","article-title":"Performance of XGBoost Ensemble Learning Algorithm for Mangrove Species Classification with Multisource Spaceborne Remote Sensing Data","volume":"4","author":"Zhen","year":"2024","journal-title":"J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, Y., Meng, Z., Zu, J., Cai, W., Wang, J., Su, H., and Yang, J. (2024). Fine-scale mangrove species classification based on uav multispectral and hyperspectral remote sensing using machine learning. Remote Sens., 16.","DOI":"10.3390\/rs16163093"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112134","DOI":"10.1016\/j.ymssp.2024.112134","article-title":"Surrogate modeling of pantograph-catenary system interactions","volume":"224","author":"Cheng","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lu, Y., Chen, Y., Zhao, D., and Chen, J. (2019). Graph-FCN for image semantic segmentation. Advances in Neural Networks, Proceedings of the International Symposium on Neural Networks, Moscow, Russia, 10\u201312 July 2019, Springer.","DOI":"10.1007\/978-3-030-22796-8_11"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107260","DOI":"10.1016\/j.engappai.2023.107260","article-title":"Image semantic segmentation approach based on DeepLabV3 plus network with an attention mechanism","volume":"127","author":"Liu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018, January 8\u201314). Unified perceptual parsing for scene understanding. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"ref_25","unstructured":"Howard, A.G. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_26","unstructured":"Yuan, Y., Chen, X., Chen, X., and Wang, J. (1909). Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation. arXiv."},{"key":"ref_27","unstructured":"He, J., Deng, Z., and Qiao, Y. (November, January 27). Dynamic multi-scale filters for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"LabelMe: A database and web-based tool for image annotation","volume":"77","author":"Russell","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","first-page":"4","article-title":"ISPRS semantic labeling contest","volume":"1","author":"Rottensteiner","year":"2014","journal-title":"ISPRS Leopoldsh\u00d6he Germany"},{"key":"ref_32","unstructured":"Wang, J., Zheng, Z., Ma, A., Lu, X., and Zhong, Y. (2021). LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation. arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/461\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:56:23Z","timestamp":1760028983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/461"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030461"],"URL":"https:\/\/doi.org\/10.3390\/sym17030461","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,3,19]]}}}