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轻量级遥感变化检测与渐进的多尺度差异聚合

Yinghua Fu¹, Haifeng Peng¹, Tingting Zhao¹

¹School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Scientific reports

|August 18, 2025

概括

本研究介绍了一种轻量级的深度学习模型,用于远程传感变化检测 (CD). 拟议的移动CDNet显著降低了计算成本和参数,同时在基准数据集上实现了高精度.

科学领域:

遥感遥感遥感遥感
地理空间分析是什么
计算机视觉计算机视觉

背景情况:

遥感中的变化检测 (CD) 通过使用多时间图像分析地面表面变化.
深度学习模型提供先进的特征表示,但通常需要大量的计算资源.
现有的轻量级CD方法可能会忽略关键的浅功能.

研究的目的:

开发一个计算效率高,重量轻的深度学习网络,用于远程传感变化检测.
解决目前基于神经网络的CD方法中高参数数量和计算需求的局限性.
通过融合浅层和深层特征来提高检测到的变化的代表性.

主要方法:

提出了一个新的轻量级网络,将MobileNetV2作为编码器和修改后的UNet作为解码器结合起来.
利用MobileNetV2来有效地从双时间遥感图像中提取特征.
在UNet解码器中实现差异图像的层次融合,以改进变化表示.

主要成果:

拟议的移动CDNet在比较轻量级网络中实现了最低的计算成本 (2.38G) 和最小的参数 (2.95M).
在三个公共数据集 (SYSU-CD,BCDD,LEVIR-CD) 上表现出卓越的性能,F1分数分别为82.84%,94.51%和90.89%.
验证了拟议的架构在准确识别地面表面变化的有效性.

结论:

开发的移动CDNet为远程传感变化检测提供了实用和高效的解决方案.
融合战略通过利用多层次特征,有效地提高了细微变化的检测.
该方法为具有有限计算资源的应用提供了有价值的替代方案.

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