摘要
为了将遥感场景图像更好地进行分割,供给下游任务使用,提出多尺度注意力提取与全局信息重建网络.编码器引入多尺度卷积注意力骨干到遥感深度学习语义分割模型中.多尺度卷积注意力能够捕获多尺度信息,给解码器提供更丰富的全局深浅层信息.在解码器,设计了全局多分支局部Transformer块.多尺度逐通道条带卷积重建多尺度空间上下文信息,弥补全局分支存在的空间信息割裂,与全局语义上下文信息共同重建全局信息分割图.解码器末端设计极化特征精炼头.通道上利用softmax和sigmoid组合,构建概率分布函数,拟合更好的输出分布,修复浅层中潜在的高分辨率信息损失,指导和融合深层信息,获得精细的空间纹理.实验结果表明,网络实现了很高的精确度,在ISPRS Vaihingen数据集上达到82.9%的平均交并比,在ISPRS Potsdam数据集上达到87.1%的平均交并比.
A network for multi-scale attention extraction and global information reconstruction was proposed in order to enhance the segmentation of remote sensing scene images for downstream tasks.A multi-scale convolutional attention backbone was introduced into the remote sensing deep learning semantic segmentation model in the encoder.Multi-scale convolutional attention can capture multi-scale information and provide richer global deep and shallow information to the decoder.A global multi-branch local Transformer block was designed in the decoder.Multi-scale channel-wise striped convolution reconstructed multi-scale spatial context information,compensating for the spatial information fragmentation in the global branch.The global information segmentation map was reconstructed together with global semantic context information.A polarized feature refinement head was designed at the end of the decoder.A combination of softmax and sigmoid was used to construct a probability distribution function on the channel,which fitted a better output distribution,repaired potential high-resolution information loss in shallow layers,guided and integrated deep information.Then fine spatial texture was obtained.The experimental results showed that high accuracy was achieved by the network,with a mean intersection over union(MIoU)of 82.9%on the ISPRS Vaihingen dataset and 87.1%on the ISPRS Potsdam dataset.
作者
梁龙学
贺成龙
吴小所
闫浩文
LIANG Longxue;HE Chenglong;WU Xiaosuo;YAN Haowen(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;School of Surveying and Mapping and Geographic Information,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第11期2270-2279,2319,共11页
Journal of Zhejiang University：Engineering Science
基金
国家重点研发计划资助项目(2022YFB3903604)
甘肃省自然科学基金资助项目(21JR7RA310)
兰州交通大学青年科学基金资助项目(2021029)。
