轻量级的多尺度信息聚合网络用于土地覆盖,土地使用,远程传感图像的语义细分
Yahia Said1, Oumaima Saidani2, Ali Delham Algarni3
1Center for Scientific Research and Entrepreneurship, Northern Border University, 73213, Arar, Saudi Arabia.
Scientific reports
|August 18, 2025
概括
本研究介绍了一种轻量级的神经网络,用于高效地表和土地利用 (LCLU) 细分. 该模型在低功耗设备上实现了高精度,使实时远程传感分析成为可能.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理空间分析是什么
背景情况:
- 土地覆盖和土地利用 (LCLU) 的细分对于环境监测,城市规划和灾害管理至关重要.
- 传统模型因高计算需求而难以在资源有限的设备上实时处理和部署.
研究的目的:
- 开发一个轻量级的神经网络,以实现高效准确的LCLU细分.
- 为了在资源有限的设备上实现实时LCLU分析.
主要方法:
- 集成密集扩展卷曲和金字塔深度卷曲用于多尺度特征提取.
- 一种编码器-解码器架构,具有密集的连接,以聚合空间和上下文信息.
- 对NITRDrone和UDD6数据集的评估.
主要成果:
- 实现了94.8%的细分精度.
- 与最先进的方法相比,参数数量显著减少.
- 在低功耗设备上实时LCLU分析的可行性.
结论:
- 轻量级神经网络为远程传感图像处理提供了可扩展和高效的解决方案.
- 拟议的模型推进了地理空间分析中的实际应用.
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