具有适应性多尺度边界意识机制的双流混合架构,用于智能城市的强有力的城市变化检测
Israr Ahmad1, Fengjun Shang1, Muhammad Salman Pathan2
1Department of Computer Science & Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
Scientific reports
|August 21, 2025
概括
这项研究引入了双流混合架构 (DSHA),用于改进远程传感中的城市变化检测. 这种新方法可以加强对土地覆盖面和基础设施变化的监测,
科学领域:
- 遥感技术
- 计算机视觉
- 城市规划
背景情况:
- 城市环境不断变化,需要有效的监测才能实现可持续发展.
- 远程传感变化检测的深度学习方法经常与复杂的场景如多尺度特征和不精确的边界作斗争.
- 现有的每像素标签方法在准确检测微妙的变化和处理域移动方面存在局限性.
研究的目的:
- 提出一种新的双流混合架构 (DSHA) 进行强大而准确的城市变化检测.
- 解决当前深度学习方法在处理复杂的城市环境变化方面的局限性.
- 通过改善土地覆盖和基础设施变化检测,加强智慧城市监测应用.
主要方法:
- 开发了一种双流混合架构 (DSHA),集成ResNet34和修改的金字塔视觉变压器 (PVT-v2).
- 在解码器中集成了一个边界感知模块和多尺度的注意力,用于精确的对象边界检测.
- 使用LEVIR-MCI数据集进行实验验证和性能评估.
主要成果:
- 在Levir-MCI数据集中,DSHA实现了92.28%的平均交叉和92.50%的F1得分.
- 废除研究证实了单个DSHA成分的显著性能改善.
- 与现有的最先进的方法相比,DSHA在基准数据集上表现出更好的表现.
结论:
- 拟议的DSHA在准确可靠的城市变化检测方面取得了重大进展.
- 该架构有效地处理复杂的场景,包括多尺度的特征和不精确的边界.
- DSHA显示了智能城市监测应用以可持续城市发展为重点的巨大潜力.
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