{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:36:50Z","timestamp":1760060210614,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Mangroves play a crucial role in ecosystems, and the accurate classification and real-time monitoring of mangrove species are essential for their protection and restoration. To improve the segmentation performance of mangrove UAV remote sensing images, this study performs species segmentation after the super-resolution (SR) reconstruction of images. Therefore, we propose SwinNET, an SR reconstruction network. We design a convolutional enhanced channel attention (CEA) module within a network to enhance feature reconstruction through channel attention. Additionally, the Neighborhood Attention Transformer (NAT) is introduced to help the model better focus on domain features, aiming to improve the reconstruction of leaf details. These two attention mechanisms are symmetrically integrated within the network to jointly capture complementary information from spatial and channel dimensions. The experimental results demonstrate that SwinNET not only achieves superior performance in SR tasks but also significantly enhances the segmentation accuracy of mangrove species.<\/jats:p>","DOI":"10.3390\/sym17081250","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"1250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Super Resolution for Mangrove UAV Remote Sensing Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1743-3139","authenticated-orcid":false,"given":"Qin","family":"Qin","sequence":"first","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Wenlong","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Guilin University of Electronic Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, M., Liu, M., Wang, Z., Mao, D., Ren, C., and Cui, H. (2016). Evaluating the effectiveness of conservation on mangroves: A remote sensing-based comparison for two adjacent protected areas in Shenzhen and Hong Kong, China. Remote Sens., 8.","DOI":"10.3390\/rs8080627"},{"key":"ref_2","first-page":"3698","article-title":"Analysis of physiological structure parameters of Shankou mangrove based on Sentinel-2 data and space-time characteristics","volume":"21","author":"Shiwen","year":"2021","journal-title":"Sci. Technol. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s44274-025-00207-9","article-title":"Rapid mangrove dieback in the northern Persian Gulf driven by anthropogenic activities and environmental stressors","volume":"3","author":"Kabiri","year":"2025","journal-title":"Discov. Environ."},{"key":"ref_4","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_5","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_6","first-page":"492","article-title":"Comparison of mangrove remote sensing classification based on multi-type UAV data","volume":"39","author":"Kai","year":"2019","journal-title":"Trop. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102200","DOI":"10.1016\/j.ecoinf.2023.102200","article-title":"Segmentation of individual mangrove trees using UAV-based LiDAR data","volume":"77","author":"You","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, Y., Ca, J., Qin, Q., Feng, Y., and Yan, J. (2024). Semantic segmentation network for mangrove tree species based on UAV remote sensing images. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-81511-x"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11852-020-00780-6","article-title":"Mapping coastal ecosystems and features using a low-cost standard drone: Case study, Nayband Bay, Persian gulf, Iran","volume":"24","author":"Kabiri","year":"2020","journal-title":"J. Coast. Conserv."},{"key":"ref_10","first-page":"486","article-title":"Identification of mangrove canopy species based on visible unmanned aerial vehicle images","volume":"40","author":"Wen","year":"2020","journal-title":"J. For. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123881","DOI":"10.1016\/j.eswa.2024.123881","article-title":"Simulated multimodal deep facial diagnosis","volume":"252","author":"Jin","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TETCI.2024.3377728","article-title":"Transformer and graph convolution-based unsupervised detection of machine anomalous sound under domain shifts","volume":"8","author":"Yan","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_14","first-page":"4404512","article-title":"Collaborative network for super-resolution and semantic segmentation of remote sensing images","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_16","unstructured":"Vaswani, A. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_18","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, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hassani, A., Walton, S., Li, J., Li, S., and Shi, H. (2023, January 17\u201324). Neighborhood attention transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00599"},{"key":"ref_20","unstructured":"Dong, C., Loy, C.C., and Tang, X. (2016, January 11\u201314). Accelerating the super-resolution convolutional neural network. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part II 14."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201322). Residual dense network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/TPAMI.2020.3021088","article-title":"Densely residual laplacian super-resolution","volume":"44","author":"Anwar","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., and Zhang, L. (2019, January 16\u201320). Second-order attention network for single image super-resolution. Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_27","first-page":"3499","article-title":"Cross-scale internal graph neural network for image super-resolution","volume":"33","author":"Zhou","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., and Zhang, L. (2022, January 23\u201327). Efficient long-range attention network for image super-resolution. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., and Zhang, L. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:24:10Z","timestamp":1760034250000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/8\/1250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,6]]},"references-count":29,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["sym17081250"],"URL":"https:\/\/doi.org\/10.3390\/sym17081250","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,8,6]]}}}