Swin-UperNet: A Semantic Segmentation Model for Mangroves and Spartina alterniflora Loisel Based on UperNet

Abstract

As an ecosystem in transition from land to sea, mangroves play a vital role in wind and wave protection and biodiversity maintenance. However, the invasion of Spartina alterniflora Loisel seriously damages the mangrove wetland ecosystem. To protect mangroves scientifically and dynamically, a semantic segmentation model for mangroves and Spartina alterniflora Loise was proposed based on UperNet (Swin-UperNet). In the proposed Swin-UperNet model, a data concatenation module was proposed to make full use of the multispectral information of remote sensing images, the backbone network was replaced with a Swin transformer to improve the feature extraction capability, and a boundary optimization module was designed to optimize the rough segmentation results. Additionally, a linear combination of cross-entropy loss and Lovasz-Softmax loss was taken as the loss function of Swin-UperNet, which could address the problem of unbalanced sample distribution. Taking GF-1 and GF-6 images as the experiment data, the performance of the Swin-UperNet model was compared against that of other segmentation models in terms of pixel accuracy (PA), mean intersection over union (mIoU), and frames per second (FPS), including PSPNet, PSANet, DeepLabv3, DANet, FCN, OCRNet, and DeepLabv3+. The results showed that the Swin-UperNet model achieved the best PA of 98.87% and mIoU of 90.0%, and the efficiency of the Swin-UperNet model was higher than that of most models. In conclusion, Swin-UperNet is an efficient and accurate model for mangrove and Spartina alterniflora Loise segmentation synchronously, which will provide a scientific basis for Spartina alterniflora Loise monitoring and mangrove resource conservation and management.