注意力引导与多特征融合的遥感影像分割  

Remote Sensing Image Segmentation Based on Attention Guidance and Multi-Feature Fusion

作　　者：张印辉[1] 张枫 何自芬[1] 杨小冈 卢瑞涛 陈光晨 Zhang Yinhui;Zhang Feng;He Zifen;Yang Xiaogang;Lu Ruitao;Chen Guangchen(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,Yunnan,China;College of Missile Engineering,Rocket Force Engineering University,Xi'an 710025,Shaanxi,China)

机构地区：[1]昆明理工大学机电工程学院,云南昆明650500 [2]火箭军工程大学导弹工程学院,陕西西安710025

出　　处：《光学学报》2023年第24期345-357,共13页Acta Optica Sinica

基　　金：国家自然科学基金(62061022,62171206)。

摘　　要：从遥感影像能够获取到精度高、范围广的地物信息,因而遥感影像在高空侦察和精确制导等领域得到广泛应用。针对遥感影像地物目标边缘模糊、尺度多变导致难以精准分割的问题,提出以深度残差网络为主干并结合注意力引导与多特征融合的分割方法,命名为AMSNet。首先,采用类别引导通道注意力模块提高模型对难分辨区域的敏感性;其次,嵌入特征复用模块减少遥感影像特征提取过程中边缘损失和小尺度目标丢失的问题;最后,设计跨区域特征融合模块以增强对多尺度特征信息的获取能力,并耦合多尺度损失融合模块对损失函数进行优化,综合提升模型对多尺度遥感影像目标的分割能力。选取3组遥感影像数据集进行对比实验,结果表明,AMSNet能够有效分割遥感影像地物目标边缘和多尺度目标。Objective Remote sensing images have a large detection range,long dynamic monitoring time,and a large amount of carrying information,making the obtained ground feature information more comprehensive and rich.By extracting ground object targets from remote sensing images,more detailed and accurate ground object information in the imaging area can be obtained,providing data support for high-altitude reconnaissance,precision guidance,and terrain matching.However,with the rapid increase in data volume,the current low level of intelligent and automated target extraction methods is difficult to embrace the demand.Traditional image extraction techniques contain edge detection,threshold segmentation,and region segmentation.These methods have good segmentation performance for remote sensing targets with significant contour boundaries but lack the ability of adaptive adjustment while facing complex and ever-changing remote sensing targets.Convolutional neural networks have stronger representation ability,scalability,and robustness than traditional methods by providing multi-level semantic information in images.Due to the uneven distribution,blurred edges,and variable scales of ground objects in remote sensing images,convolutional neural networks are prone to losing edge information and multi-scale feature information during feature extraction.In addition,cloud cover of remote sensing targets in complex scenes exacerbates the loss of target edge and multi-scale information,making it more difficult for convolutional neural networks to accurately segment remote sensing ground objects.In order to solve the above problems,we propose a segmentation method that uses deep residual networks as the backbone and combines attention guidance and multi-feature fusion to enhance the network's ability to segment remote sensing image ground object edges and multi-scale objects.Methods We propose a remote sensing image semantic segmentation network called AMSNet,which combines attention guidance and multi-feature fusion.In the Encoder Secti

关 键 词：遥感影像 语义分割 注意力机制 多尺度特征 

分 类 号：P237[天文地球—摄影测量与遥感]