摘要
为支持对遥感图像中地物目标的快速识别,提出一种基于改进U-Net神经网络的目标提取算法,选用经典的深度学习神经网络U-Net作为主干网络,提出了一种改进的U-Net网络架构,在编码器部分添加密集连接减轻(wide-range attention unit,WRAU)的网络退化问题和添加宽范围注意单元更好地融合多尺度特征通道,并在Massachusetts以及DeepGlobe数据集上进行评估,实验结果验证了所提网络架构的性能,相较于U-Net、ResUNet、UNetPPL、E-Net、SegNet等网络的优势.探讨了深度学习在遥感图像目标检测领域未来的研究趋势.
To support the fast recognition of ground targets in remote sensing images,an improved U-Net neural network-based target extraction algorithm is proposed,the classical deep learning neural network U-Net is selected as the backbone network,dense connections in the encoder part is added to alleviate the network degradation problem of Wide-Range Attention Unit(WRAU)and is added to better fuse multi-scale feature channels.Massachusetts and DeepGlobe datasets are evaluated.The experimental results validate that the superiority performance of WRAU-Net compared with that of U-Net,ResUNet,UNetPPL,E-Net,SegNet and other networks.Finally,the future research trends of deep learning in the field of remote sensing image target detection is discussed in the conclusion section.
作者
孙岩
吴熙曦
雷震
SUN Yan;WU Xixi;LEI Zhen(Army Academy of Armored Forces,Beijing 100072,China)
出处
《指挥与控制学报》
CSCD
2023年第5期596-605,共10页
Journal of Command and Control
关键词
目标检测
深度学习
卷积神经网络
遥感图像
target detection
deep learning
convolution neural network(CNN)
remote sensing image
