结合多路径的高分辨率遥感影像建筑物提取SER-UNet算法

SER-UNet algorithm for building extraction from high-resolution remote sensing image combined with multipath

针对深层卷积较难兼顾全局特征与局部特征从而导致提取建筑物边缘不准确和微小建筑物丢失的问题,以注意力机制和跳跃连接为基础提出SER-UNet算法。SER-UNet算法在编码器阶段耦合SE-ResNet和最大池化层,在解码器阶段关联SE-ResNet与反卷积层,通过跳跃连接将编码器提取的浅层特征和解码器提取的深层特征进行融合后输出特征图。验证SER-UNet算法的有效性,在MAP-Net网络并行多路径特征提取阶段使用SER-UNet算法替换原网络中的特征提取结构,分别在WHU数据集和Inria数据集上进行评估,IoU与精度分别达91.46%、82.61%和95.67%、92.75%,对比UNet、PSPNet、ResNet101、MAP-Net网络,IoU分别提高0.49%、0.14%、1.89%、1.57%,精度分别提高0.14%、1.06%、2.42%、1.09%。分析SER-UNet算法的泛化能力,将级联SER-UNet的MAP-Net网络在AerialImage数据集上进行提取验证,IoU与精度分别达85.32%和94.13%。结果表明,结合SER-UNet算法的MAP-Net并行多路径网络表现出较好的提取精度与泛化能力。此外,SER-UNet算法可以有效地嵌入PSPNet、ResNet101、HRNetv2等网络中,提升网络特征表示能力。

Aiming at the problems of inaccurate edges and loss of small buildings in the extracted buildings due to the inability of deep convolution to take into account global features and local features,the SER-UNet algorithm is proposed based on attention mechanism and skip connection.SER-UNet algorithm couples SE_ResNet and max pooling layers in the encoder stage,and the SE_ResNet structure and deconvolution are used in the decoder stage.The feature map is output after fusing the shallow features extracted by the encoder and the deep features extracted by the decoder through skip connections.In order to analyze the effectiveness of the method,the SER-UNet is used to replace the feature extraction structure in the original network in the parallel multi-path feature extraction stage of the MAP-Net network.Finally,the method proposed is experimentally evaluated on the WHU dataset and the Inria dataset,and the IoU and precision reach 91.46%,82.61%and 95.67%,92.75%,compared with UNet,PSPNet,ResNet101,and MAP-Net Networks,the IoU is increased by 0.49%,0.14%,1.89%,and 1.57%,and the precision is increased by 0.14%,1.06%,2.42%and 1.09%,respectively.To further analyze the validity of the SER-UNet algorithm,the edge integrity and small extraction verification IoU and precision reached 85.32%and 94.13%on the AerialImage dataset.The experiment results show that the MAP-Net parallel multipath network combined with SER-UNet algorithm shows good generalization ability.In addition,the SER-UNet algorithm can be effectively embedded in PSPNet,ResNet101,HRNetv2 and other Networks to improve the ability of Network feature representation.