College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel). 2023 Jun 3;23(11):5310. doi: 10.3390/s23115310.
Efficient processing of ultra-high-resolution images is increasingly sought after with the continuous advancement of photography and sensor technology. However, the semantic segmentation of remote sensing images lacks a satisfactory solution to optimize GPU memory utilization and the feature extraction speed. To tackle this challenge, Chen et al. introduced GLNet, a network designed to strike a better balance between GPU memory usage and segmentation accuracy when processing high-resolution images. Building upon GLNet and PFNet, our proposed method, Fast-GLNet, further enhances the feature fusion and segmentation processes. It incorporates the double feature pyramid aggregation (DFPA) module and IFS module for local and global branches, respectively, resulting in superior feature maps and optimized segmentation speed. Extensive experimentation demonstrates that Fast-GLNet achieves faster semantic segmentation while maintaining segmentation quality. Additionally, it effectively optimizes GPU memory utilization. For example, compared to GLNet, Fast-GLNet's mIoU on the Deepglobe dataset increased from 71.6% to 72.1%, and GPU memory usage decreased from 1865 MB to 1639 MB. Notably, Fast-GLNet surpasses existing general-purpose methods, offering a superior trade-off between speed and accuracy in semantic segmentation.
随着摄影和传感器技术的不断进步，人们越来越需要高效地处理超高分辨率的图像。然而，遥感图像的语义分割在优化 GPU 内存利用率和特征提取速度方面仍缺乏令人满意的解决方案。针对这一挑战，Chen 等人引入了 GLNet，这是一种旨在处理高分辨率图像时在 GPU 内存使用和分割精度之间取得更好平衡的网络。在 GLNet 和 PFNet 的基础上，我们提出的方法 Fast-GLNet 进一步增强了特征融合和分割过程。它分别在局部和全局分支中引入了双特征金字塔聚合（DFPA）模块和 IFS 模块，从而生成了更优的特征图并优化了分割速度。大量实验表明，Fast-GLNet 在保持分割质量的同时实现了更快的语义分割，并且有效地优化了 GPU 内存利用。例如，与 GLNet 相比，Fast-GLNet 在 Deepglobe 数据集上的 mIoU 从 71.6%提高到了 72.1%，GPU 内存使用量从 1865MB 降低到了 1639MB。值得注意的是，Fast-GLNet 超越了现有的通用方法，在语义分割的速度和准确性方面实现了更好的权衡。
