在遥感图像中采用异质双解码器网络
Shenming Qu1, Gaigai Liu1, Xiangnan Zhang1
1Henan University, Software College, Kaifeng, 475000, China.
Scientific reports
|August 27, 2025
概括
一个新的异质双解码器网络 (HDDNet) 通过使用互补解码器来改善远程传感图像的道路提取. 这种方法提高了自动驾驶和城市规划应用的准确性.
科学领域:
- 计算机视觉
- 遥感技术
- 地理信息系统
背景情况:
- 精确地从遥感数据中提取道路信息对于自动驾驶和城市规划等应用至关重要.
- 现有的方法面临规模变化,封闭和模糊的道路边界的挑战.
研究的目的:
- 提出一种新型网络,即异质双解码器网络 (HDDNet),用于强大的道路开采.
- 解决遥感道路采集中的尺度变化,封闭和边界定义的局限性.
主要方法:
- 开发了两个互补的解码器HDDNet:用于形态和多尺度特征的动态蛇群扩展 (DSGD) 主解码器,以及用于连接和边界改进的多方向连接和边界增强 (MCBE) 的辅助解码器.
- 集成了一个双重注意力功能融合 (DAFF) 模块,用于两个解码器之间的交互空间和通道智能功能融合.
主要成果:
- 在DeepGlobe,Ottawa和CHN6-CUG数据集上实现了最先进的性能.
- 报告的交叉对欧盟 (IoU) 分分别为71.36%,91.85%和67.27%,显示出与现有方法相比的显著改进.
- 在各种道路场景中验证了HDDNet的稳定性和准确性.
结论:
- HDDNet有效地解决了遥感道路开采方面的挑战.
- 拟议的DSGD,MCBE和DAFF模块有助于增强特征表示和提取精度.
- HDDNet为依赖精确路线绘图的关键应用提供了强大的解决方案.
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