摘要
针对海岸线遥感图像存在的不规则边界精细化分割困难的问题,本文提出了非对称性多路解码的海岸线分割网络(AMDNet)。以Deeplabv3+作为主干网络,通过使用EfficientNet-B0作为特征提取器,大幅降低网络计算量,并在改进的ASPP中引入D-LKA模块,添加额外的偏移量来调整标准卷积的采样位置,允许卷积核灵活调整采样网格,结合DUpsampling技术实现上采样过程中的高精度还原,提高图像分割的精确度。AMDNet模型在Aerial photo-maps数据集上的准确率、灵敏度、Dice和Jaccard分别达到了96.77%、93.03%、90.42%、86.67%,性能提升明显。
Aiming at the difficulty in fine segmentation of irregular boundaries in coastal remote sensing images,this paper proposes an Asymmetric Multi-path Decoding Network for Coastline Segmentation(AMDNet).Taking Deeplabv3+as the backbone network,the network uses EfficientNet-B0 as the feature extractor to significantly reduce the computational load of the network.Additionally,the D-LKA module is introduced into the improved ASPP to add extra offsets for adjusting the sampling positions of standard convolution,allowing the convolution kernel to flexibly adjust the sampling grid.Combined with DUpsampling technology to achieve high-precision restoration during the upsampling process,the accuracy of image segmentation is improved.The accuracy,sensitivity,Dice and Jaccard of the AMDNet model on the Aerial photo-maps dataset reach 96.77%,93.03%,90.42%and 86.67%respectively,showing a significant performance improvement.
作者
连帅
Lian Shuai(Chinese Flight Test Establishment,Xi′an 710089,China)
出处
《电子测量技术》
北大核心
2025年第24期51-58,共8页
Electronic Measurement Technology
