ArticleScientific reports2022
Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Combining rEW-2DCOS and mechanism-guided adaptive ensemble learning to improve the retrieval of leaf nitrogen, phosphorus, and potassium contents.Plant phenomics (Washington, D.C.) · 2025Article
- The segmentation of nanoparticles with a novel approach of HRUScientific reports · 2025Article
- Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm.Plant methods · 2023Article
- A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.Frontiers in plant science · 2022Article
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8 authors.
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Abstract
Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. The specific conclusions of this paper include the followings: (1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.
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