V型Transformer的遥感影像障碍物提取方法
The method of obstacle extraction about remote sensing image based on V-Transformer
邓飞 1罗文 1蒋先艺 2许银坡 2王岩2
作者信息
- 1. 成都理工大学计算机与网络安全学院,四川成都 610059
- 2. 中国石油东方地球物理公司,河北涿州 072751
- 折叠
摘要
遥感影像中的障碍物是地震采集观测系统变观的重要依据之一.传统的人工提取障碍物方法效率低,且易受人为因素影响,难以保证结果的一致性,不适用于复杂地表环境及数量庞大的障碍物.当前通用的卷积神经网络自动提取障碍物方法,由于卷积核的尺寸受限,无法直接进行远距离的语义交互,也不能准确提取具有较大跨度且部分被遮蔽的障碍物(乡间道路、河流等).为此,提出了基于V型全自注意力网络(MTNet)提取遥感影像障碍物的方法.首先,MTNet采用端到端的V型编码器—解码器结构,通过跳跃连接实现信息交互;其次,用具有远距离建模能力的Mix-Transformer模块取代传统卷积层,提取和重建更准确的障碍物多尺度特征;最后,用轻量的块扩展层取代转置卷积,实现上采样和图像分割,重建障碍物信息.实验结果表明,该网络分割障碍物的精度和速度显著优于现有方法,尤其在道路识别方面,优势更明显.
Abstract
Obstacles in remote sensing images are the most important bases for the variable geometry of observa-tion systems in seismic exploration.The traditional manual obstacle extraction methods are inefficient and sus-ceptible to human factors,and difficult to ensure result consistency,making them unsuitable for complex sur-face environments and large numbers of obstacles.Current generalized methods for automatic obstacle extrac-tion with convolutional neural networks are limited by the size of convolution kernels,unable to directly per-form semantic interactions over long distances,and fail to accurately extract obstacles with large spans that are partially occluded(country roads,rivers,etc.).Therefore,this study proposes a V-shaped fully self-attention network(MTNet)to extract obstacles from remote sensing images.Firstly,MTNet adopts an end-to-end V-shaped encoder-decoder structure to realize information interaction through skip connections;Secondly,the tra-ditional convolutional layer is replaced by the Mix-Transformer block with long-range modeling capability to ex-tract and reconstruct more accurate multi-scale features of the obstacle;Finally,the transposed convolution is replaced by the light-weight block extending layer for upsampling and image segmentation to reconstruct the ob-stacle information.Experimental results show that the network significantly outperforms existing methods in terms of accuracy and speed in segmenting obstacles,especially in road recognition.
关键词
观测系统变观/深度学习/障碍物提取/图像语义分割/Mix-TransformerKey words
variable geometry of observation system/deep learning/obstacle extraction/image semantic segmen-tation/Mix-Transformer引用本文复制引用
基金项目
国家重点研发计划(2023YFB3905004)
中国石化地球物理实验室项目(33550006-22-FW0399-0022)
出版年
2024
国家科技期刊平台
NSTL
万方数据