Growing global population, changing climate, and shrinking land resources demand for quicker, efficient, and
more accurate methods of mapping and monitoring vegetation cover in remote sensing datasets. Many deep
learning-based methods have been widely applied for semantic segmentation tasks in remote sensing images of
vegetated environments. However, most existing models are pixel-based, which introduces challenges such as
high time consumption, cumbersome implementation, and limited scalability. This paper presents the SAGRNet
model, a Graph Convolutional Neural Network (GCN) that incorporates sampling aggregation and self-attention
mechanisms, while leveraging the ResNet residual network structure. A key innovation of SAGRNet is its ability
to fuse features extracted through diverse algorithms, enabling comprehensive representation and enhanced
classification performance. The SAGRNet model demonstrates superior performance over leading pixel-based
neural networks, such as U-Net++ and DeepLabV3, in terms of both time efficiency and accuracy in vegetation image classification tasks. We achieved an overall mapping accuracy of ~90 % using SAGRNet, compared to
~87% and ~85% from U-Net++ and DeepLabV3, respectively. Additionally, it offers more convenience in data
processing. Furthermore, the model significantly outperforms cutting-edge graph-based convolutional networks,
including Graph U-Net (achieved overall accuracy ~65%) and TGNN (achieved overall accuracy ~75%),
showcasing exceptional generalization capability and classification accuracy. This paper provides a comprehensive analysis of the various processing aspects of this object-based GCN for vegetation mapping and emphasizes its significant potential for practical use. The model’s versatility can also be expanded to other image
processing domains, offering unprecedented possibilities of information extraction from satellite imagery
