摘要
针对遥感图像道路分割边界模糊和遮挡难以区分的问题,提出了基于改进DeeplabV3+的遥感图像道路分割模型。该模型在主干网络中引入MobileNetV3和高效通道注意力机制(ECA),减少了参数量并关注连续的道路特征信息。在解码过程中采用多级上采样,增强了编码器和解码器之间的紧密连接,全面保留了细节信息。同时,在ASPP模块中采用深度可分离膨胀卷积DS-ASPP,显著减少了参数量。实验结果表明,该模型在Massachusetts Roads数据集上的交并比达到了83.71%,准确率达到了93.71%,分割精度最优,模型参数量为55.57×10^(6),能够有效地避免边界模糊和遮挡导致的错漏检问题,在遥感道路分割中提高了精度和速度。
In response to the problem of fuzzy boundaries and difficulty in distinguishing occlusions in road segmentation of remote sensing images,this paper proposes a remote sensing image road segmentation model based on an improved DeeplabV3+.The model introduces MobileNetV3 and ECA attention mechanism in the backbone network to reduce parameter volume and focus on continuous road feature information.In the decoding process,multi-level upsampling is adopted to enhance the tight connection between the encoder and decoder,fully preserving detailed information.Meanwhile,deep separable dilated convolution(DS-ASPP)is used in the ASPP module to significantly reduce the number of parameters.The experimental results demonstrate that the model achieves an intersection over union(IoU)of 83.71%and an accuracy of 93.71%on the Massachusetts Roads dataset.With a parameter count of 55.57×10^(6),the model exhibits superior segmentation accuracy and effectively avoids errors and omissions caused by boundary blurring and occlusion.It enhances both precision and speed in remote sensing road segmentation.
作者
张银胜
单梦姣
钟思远
陈戈
童俊毅
单慧琳
Zhang Yinsheng;Shan Mengjiao;Zhong Siyuan;Chen Ge;Tong Junyi;Shan Huilin(College of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;School of Electronic Information Engineering,Wuxi University,Wuxi 214105,China)
出处
《国外电子测量技术》
2024年第1期189-198,共10页
Foreign Electronic Measurement Technology
基金
国家自然科学基金(62071240)
江苏省产教融合型一流课程(2022-133)
无锡学院2023年教改研究课题(XYJG2023010,XYJG2023011)项目资助。