journal article·2025-01-20Open access

Construction of Multi-Scale Fusion Attention Unified Perceptual Parsing Networks for Semantic Segmentation of Mangrove Remote Sensing Images

Xin Wang, Yu Zhang, Wenquan Xu, Hanxi Wang, Jingye Cai, Qin Qin, Qin Wang, Jing Zeng

Applied Sciences· Volume 15· Issue 2

Abstract

Mangrove forests play a crucial role in coastal ecosystem protection and carbon sequestration processes. However, monitoring remains challenging due to the forests’ complex spatial distribution characteristics. This study addresses three key challenges in mangrove monitoring: limited high-quality datasets, the complex spatial characteristics of mangrove distribution, and technical difficulties in high-resolution image processing. To address these challenges, we present two main contributions. (1) Using multi-source high-resolution satellite imagery from China’s new generation of Earth observation satellites, we constructed the Mangrove Semantic Segmentation Dataset of Beihai, Guangxi (MSSDBG); (2) We propose a novel Multi-scale Fusion Attention Unified Perceptual Network (MFA-UperNet) for precise mangrove segmentation. This network integrates Cascade Pyramid Fusion Modules, a Multi-scale Selective Kernel Attention Module, and an Auxiliary Edge Neck to process the unique characteristics of mangrove remote sensing images, particularly addressing issues of scale variation, complex backgrounds, and boundary accuracy. The experimental results demonstrate that our approach achieved a mean Intersection over Union (mIoU) of 94.54% and a mean Pixel Accuracy (mPA) of 97.14% on the MSSDBG dataset, significantly outperforming existing methods. This study provides valuable tools and methods for monitoring and protecting mangrove ecosystems, contributing to the preservation of these critical coastal environments.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing and LiDAR Applications · Remote Sensing and Land Use

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

School of Computer Science and Information Security, Guilin University of Electronic Science and Technology, Guilin 541004, China

School of Marine Engineering, Guilin University of Electronic Technology, Beihai 536000, China

School of Geographical Sciences, Harbin Normal University, Harbin 150025, China

Basic Teaching Department, Guilin University of Electronic Technology, Beihai 536000, China

Funding

Guangxi Science and Technology Major Project · Guangxi Graduate Student Innovation Project · Beihai City Science and Technology Planning Project

References (41)

Show source-provided references
1. Now or later? optimal timing of mangrove rehabilitation under climate change uncertainty10.1016/j.foreco.2021.119739 ↗
2. Land-use dynamics associated with mangrove deforestation for aquaculture and the subsequent abandonment of ponds10.1016/j.scitotenv.2021.148320 ↗
3. Dynamics of mangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 201710.1016/j.jhydrol.2020.125271 ↗
4. Differential effects of biological invasions on coastal blue carbon: A global review and meta-analysis10.1111/gcb.14426 ↗
5. Examining the response of an eastern Australian mangrove forest to changes in hydro-period over the last century10.1016/j.ecss.2020.106813 ↗
6. Xiao, H., Su, F., Fu, D., Yu, H., Ju, C., Pan, T., and Kang, L. (2021). 10-m Global Mangrove Classification Products of 2018-2020 Based on Big Data, Science Data Bank.
7. The state of the world’s mangrove forests: Past, present, and future10.1146/annurev-environ-101718-033302 ↗
8. A review of remote sensing for mangrove forests: 1956–201810.1016/j.rse.2019.111223 ↗
9. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform10.1016/j.isprsjprs.2017.07.011 ↗
10. Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images10.1016/j.ecolind.2023.110815 ↗
11. Jiang, Y., Zhang, L., Yan, M., Qi, J., Fu, T., Fan, S., and Chen, B. (2021). High-resolution mangrove forests classification with machine learning using worldview and uav hyperspectral data. Remote Sens., 13.10.3390/rs13081529 ↗
12. Deng, L., Chen, B., Yan, M., Fu, B., Yang, Z., Zhang, B., and Zhang, L. (2023). Estimation of Species-Scale Canopy Chlorophyll Content in Mangroves from UAV and GF-6 Data. Forests, 14.10.3390/f14071417 ↗
13. Lin, Y.C., Cheng, Y.T., Zhou, T., Ravi, R., Hasheminasab, S.M., Flatt, J.E., Troy, C., and Habib, A. (2019). Evaluation of UAV LiDAR for mapping coastal environments. Remote Sens., 11.10.3390/rs11242893 ↗
14. Large-scale spatial dynamics of plants: Metapopulations, regional ensembles and patchy populations10.1046/j.1365-2745.2002.00692.x ↗
15. Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images10.1109/TGRS.2020.3014312 ↗
16. Surrogate modeling of pantograph-catenary system interactions10.1016/j.ymssp.2024.112134 ↗
17. Simulated multimodal deep facial diagnosis10.1016/j.eswa.2024.123881 ↗
18. Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics
19. Fu, C., Song, X., Xie, Y., Wang, C., Luo, J., Fang, Y., and Qiu, Z. (2022). Research on the spatiotemporal evolution of mangrove forests in the Hainan Island from 1991 to 2021 based on SVM and Res-UNet Algorithms. Remote Sens., 14.10.3390/rs14215554 ↗
20. Transformer and graph convolution-based unsupervised detection of machine anomalous sound under domain shifts10.1109/TETCI.2024.3377728 ↗
21. Offline Meta-Reinforcement Learning for Active Pantograph Control in High-Speed Railways10.1109/TII.2024.3394554 ↗
22. Long, J., Shelhamer, E., and Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 3431–3440.10.1109/TPAMI.2016.2572683 ↗
23. Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2016). Pyramid scene parsing network. IEEE Comput. Soc., 2881–2890.10.1109/CVPR.2017.660 ↗
24. Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15–20). Dual attention network for scene segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00326 ↗
25. Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8–14). Psanet: Point-wise spatial attention network for scene parsing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01240-3_17 ↗
26. Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmen-tation. arXiv.
27. Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8–14). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_49 ↗
28. K-net: Towards unified image segmentation
29. Xu, J., Xiong, Z., and Bhattacharyya, S.P. (2023, January 17–24). PIDNet: A Real-Time Semantic Segmentation Network Inspired by PID Controllers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.01871 ↗
30. Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018, January 8–14). Unified perceptual parsing for scene understanding. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01228-1_26 ↗
31. Comparison of multi-source satellite images for classifying marsh vege-tation using DeepLabV3 Plus deep learning algorithm10.1016/j.ecolind.2021.107562 ↗
32. Wang, Z., Li, J., Tan, Z., Liu, X., and Li, M. (2023). Swin-UperNet: A Semantic Segmentation Model for Mangroves and Spartina alterniflora Loisel Based on UperNet. Electronics, 12.10.3390/electronics12051111 ↗
33. Li, Y., Hou, Q., Zheng, Z., Cheng, M.M., Yang, J., and Li, X. (2023). Large Selective Kernel Network for Remote Sensing Object Detection. arXiv.10.1109/ICCV51070.2023.01540 ↗
34. Wang, X., Kang, M., Chen, Y., Jiang, W., Wang, M., Weise, T., and Zhang, C. (2023). Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery. Remote Sens., 15.10.3390/rs15081980 ↗
35. Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18–24). A convnet for the 2020s. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.10.1109/CVPR52688.2022.01167 ↗
36. Zhang, T., Hu, S., He, Y., You, S., Yang, X., Gan, Y., and Liu, A. (2021). A fine-scale mangrove map of China derived from 2-meter resolution satellite observations and field data. ISPRS Int. J. Geo-Inf., 10.10.3390/ijgi10020092 ↗
37. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., and Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
38. The backscattering characteristics of wetland vegetation and water-level changes detection using multi-mode SAR: A case study
39. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21–26). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
40. FactSeg: Foreground activation-driven small object semantic segmentation in large-scale remote sensing imagery
41. Berman, M., Triki, A.R., and Blaschko, M.B. (2018, January 18–23). The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00464 ↗