基于GF-2图像的CTSA-DeepLabV3+城市绿色空间分类模型的研究
Ruotong Li1, Jian Zhao1, Yanguo Fan1
1School of Oceanography and Spatial Information, China University of Petroleum, Qingdao 266580, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
一个新的上下文变压器和挤压聚合刺激增强的DeepLabV3+ (CTSA-DeepLabV3+) 模型准确地从卫星图像中对城市绿色空间进行了分类. 这种先进的深度学习方法可以提高高精度的生态绘图和城市规划.
科学领域:
- 遥感 遥感 遥感 遥感
- 城市生态城市生态学
- 计算机视觉 计算机视觉
背景情况:
- 城市绿地对于生态平衡和城市规划至关重要.
- 从高分辨率图像中对城市绿色空间类型进行分类是具有挑战性的,原因是复杂的形态和碎片化.
研究的目的:
- 开发一个先进的深度学习模型,用于准确的城市绿色空间分类.
- 通过使用高分辨率卫星图像来增强城市绿色空间的解释.
主要方法:
- 提出了一个上下文变压器和挤压聚合激发增强的DeepLabV3+ (CTSA-DeepLabV3+) 模型.
- 集成了一个上下文转换器 (CoT) 模块用于全球上下文建模.
- 采用了SENetv2的注意力机制,以改进功能捕捉,使用Gaofen-2 (GF-2) 卫星图像.
主要成果:
- CTSA-DeepLabV3+模型的整体分类准确率达到了96.21%.
- 与DeepLabV3 +,FCN,U-Net,PSPNet和UperNet-Swin Transformer相比,其表现出了更好的性能.
- 报告的平均交叉比,精度,回忆和F1分数分别为89.22%,92.56%,90.12%和91.23%.
结论:
- CTSA-DeepLabV3+模型为城市绿色空间的分类提供了一个高度准确的解决方案.
- 提供一种高效的方法,通过高分辨率的遥感数据对城市绿色空间进行智能解释.
- 支持生态环境保护和城市空间结构优化.
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