远程传感图像的快速语义细分使用集成全球和本地信息的网络
Boyang Wu1, Jianyong Cui1, Wenkai Cui1
1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
快速GLNet增强了用于高分辨率遥感图像的语义细分. 这种新方法提高了速度和准确性,同时降低了GPU内存的使用量,比现有解决方案提供了更好的权衡.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 图像处理 图像处理
背景情况:
- 图像技术的进步需要高效处理超高分辨率图像.
- 目前用于遥感的语义细分方法在优化GPU内存和特征提取速度方面扎.
- 像GLNet这样的现有网络提供了平衡,但可以进一步改进.
研究的目的:
- 开发一种高分辨率遥感图像的高效语义细分方法.
- 增强特征融合和细分流程,以提高速度和准确性.
- 为了优化GPU内存利用,而不会影响细分质量.
主要方法:
- 提出了基于GLNet和PFNet架构的快速GLNet.
- 为当地分支机构引入了双重特征金字塔聚合 (DFPA) 模块.
- 整合了全球分支机构的IFS模块,以改善特征表示.
主要成果:
- 与基线方法相比,Fast-GLNet表现出更快的语义细分速度.
- 分段精度保持或略有提高,mIoU从Deepglobe数据集的71.6%增加到72.1%.
- 与GLNet相比,GPU内存使用量显著减少,从1865MB降至1639MB.
结论:
- 快速GLNet为高分辨率遥感图像的语义细分提供了速度和准确性之间的优越权衡.
- 拟议的DFPA和IFS模块有效地提高了特征图和细分效率.
- 快速GLNet为内存受限和速度敏感的遥感应用提供了一个有前途的解决方案.
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