A global-local multi-scale feature extraction network for fine-grained segmentation of cyanobacterial blooms and aquatic vegetation

Yunchang MU; Junsheng LI; Huan ZHAO; Xiaodi ZHU; Zeyan ZHANG; Fangfang ZHANG; Shenglei WANG

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A global-local multi-scale feature extraction network for fine-grained segmentation of cyanobacterial blooms and aquatic vegetation

Inland water color remote sensing algorithms | Updated：2026-08-13

- A global-local multi-scale feature extraction network for fine-grained segmentation of cyanobacterial blooms and aquatic vegetation
- Journal of Oceanology and Limnology Vol. 44, Pages: 1-19(2026)
- Affiliations：
  
  1.Beijing Institute of Surveying and Mapping, Beijing 100038, China
  2.Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  3.International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
  4.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  5.Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100094, China
  6.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  lijs@radi.ac.cn
- Funds：
- DOI：
  CLC：
- Received：05 January 2026，
  
  Online First：13 August 2026，
  
  Published：01 September 2026
- Accepted：
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MU Yunchang,LI Junsheng,ZHAO Huan,et al.A global-local multi-scale feature extraction network for fine-grained segmentation of cyanobacterial blooms and aquatic vegetation[J].Journal of Oceanology and Limnology,
DOI：

MU Yunchang,LI Junsheng,ZHAO Huan,et al.A global-local multi-scale feature extraction network for fine-grained segmentation of cyanobacterial blooms and aquatic vegetation[J].Journal of Oceanology and Limnology, DOI：.

摘要

Abstract

Cyanobacterial blooms and aquatic vegetation exhibit similar spectral characteristics

which limits the discriminative capability of traditional spectral index-based methods. Moreover

existing deep learning models still suffer from omission and misclassification errors in fragmented boundary regions. This study proposes a semantic segmentation network

termed global-local multi-scale feature extraction network (GLMFENet)

which is designed to jointly exploit global and local multi-scale features. Based on an encoder-decoder architecture

GLMFENet incorporates a global feature extraction module to model long-range semantic dependencies

together with a spatial attention-guided local multi-scale feature extraction module to enhance fine-grained discriminability. By synergistically integrating global contextual information with local structural features

the proposed network enables accurate differentiation between cyanobacterial blooms and aquatic vegetation. A dataset comprising 467 Sentinel-2 MSI images collected from multiple lakes across China was established for model training and validation. GLMFENet achieved a mean IoU (mIoU) of 84.6%

a mean F1-score of 91.3%

and a Kappa coefficient of 86.4% on the test set

outperforming widely adopted deep learning models such as DeepLabV3+

SegFormer

and Swin-UNet. The proposed method improves the mIoU by 10.7–19.6 percentage points compared with traditional spectral index-based methods

leading to more accurate and reliable extraction of cyanobacterial blooms and aquatic vegetation.

关键词

Keywords

references

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