Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition
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摘要: 随着人工智能技术的发展,基于深度神经网络的合成孔径雷达(SAR)目标识别得到了广泛关注。然而,SAR系统的成像机制导致了图像特性与成像参数之间的强相关性,因此深度学习框架下的目标识别算法精度极易受成像参数敏感性的干扰,这成为了制约先进智能算法部署到实际工程中的一大障碍。该文首先回顾了SAR图像目标识别技术的发展与相关数据集,从雷达工作的成像几何、载荷参数和噪声干扰3个角度,深入分析了成像参数变化对图像特性的影响;然后,从模型、数据、特征3个维度,总结归纳了现有文献关于深度学习技术对成像参数敏感性的鲁棒性与泛化性这一问题的研究进展;接下来,汇总并分析了典型方法的实验结果;最后讨论了在未来有望突破成像参数敏感性这一问题的深度学习技术研究方向。Abstract: With the development of artificial intelligence technology, Synthetic Aperture Radar (SAR) target recognition based on deep neural networks has received widespread attention. However, the imaging mechanism of SAR system leads to a strong correlation between image characteristics and imaging parameters, so the algorithm accuracy under deep learning is easily disturbed by the sensitivity of imaging parameters, which becomes a major obstacle restricting the deployment of advanced intelligent algorithms to practical engineering applications. Firstly, in this paper, the developments of SAR image target recognition technology and related data sets are reviewed, and the influence of imaging parameters on image characteristics is analyzed deeply from three aspects, i.e., imaging geometry, radar parameter and noise interference. Then, the existing literature on the robustness and generalization of deep learning technology to imaging parameter sensitivity is summarized from the three dimensions of model, data and features. Thereafter, the experimental results of typical methods are summarized and analyzed. Finally, the research direction of deep learning technology which is expected to break through the sensitivity of imaging parameters in the future is discussed.
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1. 引言
随着物联网(Internet of Things, IoT)技术的广泛应用,物联网节点数目不断增加,然而由于资源所限,节点无法完成计算密集型任务。因此面向物联网数据分析的传统机器学习通常基于集中式算法,由具备超强存储和计算能力的专用服务器对数据进行集中式地处理[1]。然而进入万物互联和大数据时代[2]以来,节点数量急剧增加的同时产生海量实时数据,基于集中式算法的传统机器学习对如此大规模的物联网数据进行分析处理时,主要面临3方面挑战:首先,直接将节点的海量数据上传至服务器,会造成网络带宽压力过大和计算资源的浪费[1];其次,传统的集中式算法在应用于求解大规模机器学习问题时可扩展性较差[3];最后,物联网数据若直接上传至服务器或其他设备进行训练,会面临数据隐私泄露的风险[4]。事实上,为了解决大数据下传统机器学习面临的问题,各分布式数据并行平台纷纷研发分布式机器学习库,然而由于资源限制[5]和隐私问题[6],它们并不适用于资源受限且异构的物联网节点。因此分布式优化算法逐渐涌现出来,将服务器难以完成的计算任务拆分成多个小计算任务分布式地部署到多个物联网节点上执行,然后将各节点的执行结果整合成最终结果并返回。相比于传统的集中式算法,分布式优化算法能够减轻网络带宽压力、降低通信成本,保护数据的隐私性。
本文主要研究如何利用分布式优化算法对物联网数据进行回归分析,重点研究目标是弹性网络回归[7]这一典型的线性回归技术。本文提出一种基于多个物联网节点的协同弹性网络回归问题模型。针对该模型,引入交替方向乘子法(Alternating Direction Method of Multipliers, ADMM)算法[1],提出一种基于ADMM的分布式弹性网络回归学习算法,将需要由服务器集中式求解的目标优化问题分解成多个可以由物联网节点进行分布式独立求解的子问题。该算法并不要求节点将原始数据上传至服务器,而是由节点独立处理数据,仅仅向服务器传递中间结果,再由服务器整合并返回最终结果。服务器与节点之间以这种协作方式进行多次迭代直至模型收敛。为了验证所提算法的有效性以及评估该算法的性能,本文在两个典型数据集上进行大量的仿真实验,结果表明:所提算法可在几十轮内快速收敛到最优解;相比于本地化算法,提高了结果的有效性和准确性;相比于集中式算法,目标函数值和预测精度可逼近集中式算法的最优值,而且可降低网络传输带宽压力,提高计算的可扩展性,保护隐私数据的安全性。
2. 问题模型
2.1 弹性网络回归
弹性网络回归作为一种典型的线性回归技术,所解决的优化问题基本形式为
min12N∑i=1(wTxi+b−yi)2+u1‖w‖1+u22‖w‖22 (1) 其中,
{xi,yi} 是数据样本,xi∈Rn 是特征向量,yi 是相应的因变量,特征权重向量w∈Rn ,截距b∈R ,正则化参数u1,u2>0 。当u1=0 时,弹性网络回归退化为岭回归;当u2=0 时,则退化为Lasso回归y=wTx+b (2) 弹性网络回归模型建立的目标是通过训练求解得到的
(w,b) 的值,根据式(2),对于给定的特征向量x∈Rn 能准确地预测出因变量y的值。虽然弹性网络回归具备很好的预测性能[8],但集中式算法增大网络带宽压力和隐私数据泄露的风险。即使采用网络安全机制,服务器端仍然存在泄露用户隐私数据的可能。2.2 问题模型
基于弹性网络回归问题模型的基本形式(1),本文进一步研究基于多个物联网节点的协同弹性网络回归学习问题。考虑由一个中心服务器与N个物联网节点组成的物联网系统,其中每个节点拥有相同的传感器组。首先,物联网节点
i∈{1,2,···,N} 在一定时间内将通过板载传感器生成的原始数据转换为特征向量,每个特征向量包含n个预测变量,即xij∈Rn ,并对应于一个因变量yij∈R ,其中j∈{1,2,···,Mi} ,Mi 是节点i提供的数据样本个数;其次,物联网节点i将数据样本Di={(xij,yij),j=1,2,···,Mi} 由本地上传至服务器,由服务器对收集到的数据进行回归分析;最后,通过建立特征向量与因变量之间的回归模型,可以通过特征向量准确地预测出因变量的值。此时弹性网络回归解决的优化问题形式为min12N∑i=1Mi∑j=1(wTxij+b−yij)2+u1‖w‖1+u22‖w‖22 (3) 其中,特征向量
xij∈Rn ,对应的因变量yij∈R ,特征权重向量w∈Rn ,截距b∈R ,正则化参数u1,u2>0 。训练所得回归模型可以根据给定的新的特征向量xij 预测出因变量yij 的值。3. 基于ADMM的分布式弹性网络回归学习算法
3.1 ADMM算法概述
ADMM算法是一个简洁高效的分布式优化算法,它独特的分布式架构而非常适用于分布式环境下的并行求解[1]。它解决的优化问题形式为
minF(x)+G(y)s.t.Ax+By=C,x∈X,y∈Y} (4) 其中,F和G是凸函数,X和Y是非空凸集,x和y是两个需要优化的原始变量,A, B, C是等式约束的参数。目标函数关于原始变量x, y可以分解成两个函数之和的形式,因此可使用ADMM算法求得最优解。
利用增广拉格朗日法[9]对原问题式(4)进行求解,可得到增广拉格朗日函数形式为
Lρ(x,y,λ)=F(x)+G(y)+λ(Ax+By−C)+ρ2‖Ax+By−C‖22 (5) 其中,
λ≥0 是对偶变量,ρ>0 是惩罚参数。原始的最小化问题式(4)转化为拉格朗日函数式(5)中原始变量
x ,y 最小化,对偶变量λ 最小化问题。在每轮迭代过程中,首先原始变量x, y进行交替优化,然后再更新对偶变量λ 。求解过程总结为xk+1=argminxLρ(x,yk,λk) (6) yk+1=argminyLρ(xk,y,λk) (7) λk+1=λk+ρ(Axk+1+Byk+1−C) (8) 文献[10]给出了ADMM算法收敛性的证明,在中等精度要求下,经过几十次迭代后即可收敛[1]。
3.2 基于ADMM的分布式弹性网络回归学习算法
由于问题模型式(3)无法根据两个变量分解成两个函数之和的形式,不能使用ADMM算法进行求解。本文引入一组辅助变量
{(wi,bi),i=1,2,···,N} ,由此可转化为与原问题等价的新的优化问题,具体形式为min12N∑i=1Mi∑j=1(wiTxij+bi−yij)2+u1‖w‖1+u22‖w‖22s.t.wi=w,bi=b,i=1,2,···,N.} (9) 其中,
{(wi,bi),i=1,2,···,N} 是物联网节点i的中间参数,{(w,b)} 是服务器整合节点的中间参数得到的全局参数。目标函数可以根据两个参数{(w,b)} 和{(wi,bi),i=1,2,···,N} 分解成两个函数之和的形式,因此可使用ADMM算法进行求解。利用增广拉格朗日法对问题式(9)进行求解,可得增广拉格朗日函数形式为Lρ(α,β,γ)=12N∑i=1Mi∑j=1(wiTxij+bi−yij)2+u1‖w‖1+u22‖w‖22+N∑i=1((wi−w)Tγi,w+(bi−b)γi,b)+N∑i=1ρ2((wi−w)T(wi−w)+(bi−b)2) (10) 其中,
α={(w,b)} ,β={(wi,bi),i=1,2,···,N} ,γ={(γi,w,γi,b),i=1,2,···,N} 。通过对拉格朗日函数式(10)中的参数α ,β 和γ 的迭代更新,可最终求得原问题的最优解,接下来将分别介绍各参数的更新过程。(1)
α -更新部分:α 更新时需要解决的优化问题具有如式(11)的形式minαk+1u1‖w‖1+u22‖w‖22+ρN2wT(w−2¯wk−2¯γwkρ)+ρN2b(b−2¯bk−2¯γbkρ) (11) 其中,
¯θk 为在第k次迭代时,向量θi(i=1,2,···,N) 的均值。该优化问题由于包含L1范数而不可微,因此本文采用次梯度演算法(subgradient algorithm)对其进行求解。令
η=ρNρN+u2(¯wk+¯γwkρ) ,φ=u1ρN+u2 ,求解结果具有如式(12)的形式wk+1={η−φ ,η>φ0,η∈[−φ,φ]η+φ ,η<−φ (12) bk+1=¯bk+¯γbkρ (13) (2)
β -更新部分:当α 通过更新得到αk+1 即{(wk+1,bk+1)} 后,β 更新时需要解决的优化问题具有如式(14)的形式minβk+112N∑i=1Mi∑j=1(wiTxij+bi−yij)2+N∑i=1ρ2wiT(wi−2wk+1+2γi,wkρ)+N∑i=1ρ2bi(bi−2bk+1+2γi,bkρ) (14) 问题式(14)可以分解成N个独立的子问题,并部署到多个物联网节点求解,节点i
minβi12Mi∑j=1(wiTxij+bi−yij)2+ρ2wiT(wi−2wk+1+2γi,wkρ)+ρ2bi(bi−2bk+1+2γi,bkρ) (15) 问题式(15)是一个典型的非线性规划问题,本文引入PRP(Polak, Ribiere and Polyar)共轭梯度法[11]对其进行求解,具体流程详见表1。首先,将问题式(15)看作关于
wi 的函数F(wi) ,令bi=bti 求得使函数F(wi) 最小化的最优解w∗i ;其次,固定wi=w∗i ,将问题式(15)看作关于bi 的函数F(bi) ,求得使函数F(bi) 最小化的最优解b∗i ;最后,两组变量以这种方式多次交替更新可以求得最优解wt+1i=w∗i ,bt+1i=b∗i 。篇幅所限,本文仅介绍wi 的求解方法,对bi 的求解同理。物联网节点i关于wi 的目标优化问题为表 1 PRP共轭梯度算法流程输入:特征向量xij;相应变量yij;服务器提供的参数α={(wk+1,bk+1)};对偶变量γk={(γi,wk,γi,bk)}; b∗i; 输出:物联网节点i的局部最优解wi∗; (1) 初始迭代次数t=0,初始向量wi0=0,收敛精度ε=1e−5,初始搜索方向p0=−g(wi0); (2) repeat /*算法进行迭代*/ (3) for j = –1:2:1 (4) if F(wit+λtpt)>F(wit+j∗pt) then (5) λt←j; (6) end if (7) end for (8) wit+1←wit+λtpt;
(9) βt←g(wit+1)T(g(wit+1)−g(wit))g(wit)Tg(wit);(10) pt+1=−g(wit+1)+βtpt; (11) t←t+1; (12) until ‖g(wit)‖≤ε; /*算法达到收敛准则,停止迭代*/ (13) wi∗←wit; F(wi)=12Mi∑j=1(wiTxij+bi−yij)2+ρ2wiT(wi−2wk+1+2γi,wkρ)+ρ2bi(bi−2bk+1+2γi,bkρ) (16) (3)
γ -更新部分:α 和β 通过更新得到αk+1={(wk+1,bk+1)} ,βk+1={(wik+1,bik+1)} 后,γ 更新为γk+1i,w=γki,w+ρ(wk+1i−wk+1) (17) γk+1i,b=γki,b+ρ(bk+1i−bk+1) (18) 图1说明了分布式弹性网络回归学习算法的计算流程。本文采用原始残差
rk 和对偶残差sk 共同作为算法的收敛标准[1],记εrel 和εabs 分别是原始残差和对偶残差的偏差阈值,取经验值为εrel=1e−2 ,εabs=1e−4 。当‖rk‖2≤εrel,‖sk‖2≤εabs 时,则视为算法达到收敛准则[1]。分布式弹性网络回归学习算法的具体流程详见表2。该算法的收敛性及收敛速度证明可参考文献[12,13],考虑到其复杂性和版面限制,本文不再赘述。表 2 分布式弹性网络回归学习算法流程输入:物联网节点的样本数据,包括特征向量xij;相应因变量yij; 输出:最终结果α={(w∗,b∗)}; (1) 服务器初始参数设置:k=0,¯w=0,¯b0=0,εrel=1e−2,εabs=1e−4; (2) 物联网节点i参数设置: k=0,γ0i,w=0,γ0i,b=0; (3)Repeat /*算法进行迭代*/ (4) 服务器整合物联网节点上传的中间参数(wki,bki)及(γki,w,γki,b),求得各变量均值¯wk,¯bk,¯γwk,¯γbk,根据式(12)和式(13)更新参数
(wk+1,bk+1),并将结果广播给物联网节点;(5) 物联网节点i根据服务器提供的参数(wk+1,bk+1)对问题式(14)进行求解得到参数(wk+1i,bk+1i); (6) 物联网节点i根据式(17)和式(18)更新对偶变量(γk+1i,w,γk+1i,b); (7) 物联网节点i向服务器发送新的中间参数(wk+1i,bk+1i)及(γk+1i,w,γk+1i,b); (8) k←k+1; (9) until ‖rk‖2≤εrel,‖sk‖2≤εabs; /*算法达到收敛准则,停止迭代*/ 4. 实验与分析
4.1 实验设置
为了验证分布式弹性网络回归学习算法的有效性及其性能,本文在两个典型数据集上进行了仿真实验,其中拟合数据集根据文献[1]的描述生成,包括1500个数据样本,每个样本包含9维特征向量和1个相应的因变量。通过使用数据集,不仅可以验证所提算法的有效性,而且能够在不考虑数据质量的前提下评估各参数对算法性能的影响以及与其它方法进行性能比较。然而由于该拟合数据集数据质量高且分布均匀,缺乏真实性。因此为了进一步评估该算法在实际应用中的性能,本文在真实数据集上进行了仿真实验。该真实数据集则为文献[14]中提到的公开疾病数据集,包含442个患者的数据样本,每个样本包含10个生理特征以及1年以后疾病级数指标。在本文实验中,将数据集按照7:3的比例划分为训练集和测试集。除特殊说明外,实验参数均设置为:
ρ=1.0,u1=0.01,u2=0.01, εrel=1e−2,εabs=1e−4 [1]。为了进一步评估所提算法的性能,本文设计了相关实验将其与传统的集中式算法以及本地化算法两种方法进行比较。4.2 实验结果
4.2.1 算法的有效性
首先,为了验证所提算法的收敛性,本文采用拟合数据集,在物联网节点数量N的不同取值下进行了多组实验,并观察到对于不同的N值,算法均具有良好的收敛性。采用集中式算法计算的目标函数值作为最优值,并以此为基准与分布式算法所得目标函数值进行对比。当
N=20 时算法的收敛性如图2所示。图2说明随着迭代次数的增加,目标函数值在前50次迭代过程中快速下降,迭代次数为100次时接近集中式算法求得的目标函数值。图3则表示‖r‖2 和‖s‖2 随迭代次数的变化,最终当迭代次数为227次时,算法达到收敛准则。实验结果表明,本文所提分布式算法可以在有限的迭代次数内收敛并接近集中式算法的目标函数值。其次,为了进一步评估所提算法的性能,本文采用校正复相关系数(
R2a )[15]和均方根误差(Root Mean Square Error, RMSE)分别评估算法模型的拟合效果和预测精度。图4和图5说明在大约前100次迭代过程中,所提算法RMSE值持续降低,R2a 值不断增加。当算法达到收敛准则时,RMSE的值为0.03987,逼近集中式算法的预测精度;R2a 的值为0.998307趋近于1,表示算法模型具备较好的拟合效果。实验结果表明本文所提算法可以在有限迭代次数内收敛得到接近集中式算法的拟合效果和预测精度。4.2.2 参数对算法性能的影响
为研究各参数的设置对算法性能的影响,本文在拟合数据集上进行了8组实验,以目标函数值和RMSE值作为算法性能的评价指标,在保证其他参数固定不变的条件下,分别评估参数
N,ρ,u1,u2 对算法性能的影响。其中参数N对算法性能的影响如图6所示,可以发现当N取不同值时,算法均能收敛,而且N值越小时,算法的收敛速度越快,但最终均能得到相同的目标函数值和RMSE值,表明算法具备较好的可扩展性。参数ρ 对算法性能的影响如图7所示,可以发现ρ 值较小时,算法可以更快地收敛,目标函数值和RMSE值较大。参数u1 和u2 对算法性能的影响分别如图8和图9所示,说明u1 对算法的收敛速度影响较小,但对目标函数值和RMSE值影响较大,u1 值越小,目标函数值和RMSE值就越小;与u1 相似,u2 值越小,目标函数值和RMSE值也越小,不同的是,u2 对算法的收敛速度影响较大,u2 值越大,算法的收敛速度越快。4.2.3 与其它方法性能比较
相比于分布式算法,本地化算法的性能很大程度上与单个物联网节点处理的本地数据集规模大小相关,因此将数据集随机均匀地划分为N个训练子集,即由N个节点独立训练,并以
R2a 和RMSE值作为评价指标,比较在N的不同取值下两种算法的性能。值得注意的是,当N=1 时本地化算法等同于集中式算法。图10(a)、图10(b)分别表示不同N值下两种算法的RMSE值和
R2a 值比较。由图10(a)可以发现,所提算法RMSE均值基本不随N值变化,始终维持在0.03左右,而本地化算法随着N值的增大,RMSE均值不断增大。同时,RMSE值之间的差值也在增加,说明由于单个物联网节点本地数据集规模较小,不同节点之间的训练结果差异较大。由图10(b)可以发现,所提算法R2a 值始终稳定在0.98左右,基本不随N值变化。相比之下,本地化算法R2a 值受N值的影响较大。在N<60 时R2a 值非常接近本文所提算法,然而当N>60 时,R2a 值显著下降,当N=80 时,其值已经降为负数,表明此时该模型对数据集并没有拟合效果。实验结果表明,在单个物联网节点的本地数据集规模较小且缺乏多样性的情况下,本地化算法训练得到的弹性网络回归模型性能较差,不仅预测精度降低而且拟合效果也显著下降。相比之下,本文所提算法可以利用多个物联网节点提供的样本数据,因而始终可以收敛到接近最优的状态并获得接近集中式算法性能的模型。4.2.4 应用于真实数据集的实验结果
为了进一步评估分布式算法在真实场景下的性能表现,本文使用相同的评价指标在疾病数据集[14]上进行了相关实验。由于篇幅所限,本文只列出相比于拟合数据集表现出显著不同的实验结果如图11—图13所示。
首先,如图11和图12所示,当
N=20 时,本文所提分布式算法的目标函数值和RMSE值在大约前5次迭代中快速下降,大约40次迭代后就已经接近集中式方法的最优解,这种快速收敛的现象与真实数据集的规模较小有关。其次,图13说明本地化算法是3种算法中性能表现最差的。当N=15 时,本地化算法的RMSE值远远大于集中式算法和本文所提分布式算法,而且其R2a 值已经降为负数,由此可以发现,当样本数据规模较小且数据缺乏多样性时,单个物联网节点很难通过对本地数据进行独立训练得到一个好的模型。最后,实验结果说明,在应用于真实数据集时,所提算法仍然能够得到接近集中式算法性能的模型。5. 结束语
本文面向物联网数据提出一种分布式弹性网络回归学习算法,该算法基于ADMM算法,将需要集中式求解的弹性网络回归目标优化问题分解为多个可以由物联网节点利用本地数据进行独立求解的子问题。该算法不要求节点向服务器上传原始数据,仅需上传中间结果,由服务器进行简单整合得到最终结果并返回。本文在两个典型数据集上的实验结果表明:该算法能够在几十轮迭代内快速收敛到最优解;所得目标函数值和预测精度接近集中式算法,相比于集中式算法,既减轻带宽压力又保护数据隐私性;相比于本地化算法,提高了计算结果的有效性和准确性。接下来的研究工作中,我们将进一步研究分布式优化算法在其他机器学习问题中的应用,以及在物联网实验中采用该算法解决实际问题,进一步评估它在实际应用中的性能表现。
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表 1 SAR目标识别开源数据集
来源 数据集 目标类型 采集\仿真平台 主要成像参数特点 实测 MSTAR[7] 军用车辆 机载SAR (1) X波段,HH极化,带宽561 MHz,分辨率0.3 m
(2) 覆盖15°,17°,30°和45° 4个俯仰角,0°~360°方位角(部分严重散焦图像被剔除)Gotcha[18,19] 民用车辆 机载SAR (1) X波段,全极化,带宽640 MHz
(2) 均匀覆盖43.7°~45°中8个俯仰角,0°~360°方位角CircularSAR[20] 军用车辆 机载SAR (1) X波段,带宽 1800 MHz,分辨率0.1 m
(2) 覆盖15°,26°,31°和45° 4个俯仰角,0°~360°方位角(部分严重散焦图像被剔除)SAR-ACD[21] 民用飞机 GF3 C波段,HH极化,分辨率1 m OpenSARShip-1.0/2.0[22,23] 民用舰船 Sential-1 C波段,VV和VH极化,分辨率20~22 m FuSAR-Ship[24] 民用舰船 GF3 C波段,HH和VV极化,分辨率1.5 m 仿真 SarSIM[25] 民用车辆 CST软件 (1) X波段,HH极化,分辨率0.3 m, 3种地面环境
(2) 覆盖15°,17°,25°,30°,35°,40°和45° 7个俯仰角,0°~360°方位角(5°为间隔)SAMPLE[26] 军用车辆 XPatch软件 (1) X波段,HH极化,带宽561 MHz,分辨率0.3 m
(2) 覆盖15°~17°俯仰角,10°~80°方位角表 2 优缺点及代表性方法特点总结
技术类型 优缺点 代表性参考文献 主要特点 模型端 (1) 提升融合后特征的物理可解释性
(2) 传统/物理特征的鲁棒性仍有提升空间文献[27] 将CNN模型与电磁散射特征融合 文献[28] 将CNN模型与传统几何特征融合 数据端 (1) 仅需在数据端操作,易于工程实现
(2) 性能受到扩增部分数据的质量影响文献[29] 使用仿射变化、图像旋转扩增训练集 文献[30] 使用生成对抗网络扩增训练集 文献[31] 使用电磁仿真数据扩增训练集 特征端 (1) 泛化性提升显著,存在理论基础
(2) 直推式学习限制实际应用场景文献[32] 在特征层上对齐分布 文献[33] 在特征层+像素层上对齐分布 文献[34] 在特征层+像素层+决策层上对齐分布 表 3 不同成像条件变化及其数据增强策略
表 4 机载SAR车辆目标数据集的成像参数
参数 FARAD Ka FARAD X miniSAR 成像地点 美国科特兰空军基地 美国新墨西哥州 美国新墨西哥州 成像时间 2015.08 2015.10 2005.05 波段 Ka X Ku 中心频率(GHz) 35.6 9.6 16.8 带宽(GHz) 5 3 3 俯仰角度(°) 26~34 26~34 26~29 分辨率(m) 0.1 0.1 0.1 最大观测距离(km) 6 12 8 表 5 舰船检测数据集中的四种星载SAR成像参数
参数 Gaofen-3 TerraSAR-X Radarsat-2 Sentinel-1 轨道高度(km) 755 514 798 693 入射角度(°) 10~60 20~55 20~45 10~60 波段 C X C C 带宽(MHz) 240 150 100 100 分辨率(m) 0.5~100 1~16 1~100 5~20 成像范围(km) 10~650 5~100 20~50 20~400 俯仰扫描角度(°) ±20 ±25 ±11 ±20 表 6 SOC与EOC条件中俯仰角变化情况
俯仰角(°) 类别数量 训练数据 测试数据 SOC(17°~15°) 17 15 10 EOC(17°~30°) 17 30 3 EOC(17°~45°) 17 45 3 表 7 MSTAR数据集上典型方法总体识别率(OA)对比(%)
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[1] 冯博迪, 杨海涛, 李高源, 等. 神经网络在SAR图像目标识别中的研究综述[J]. 兵器装备工程学报, 2021, 42(10): 15–22. doi: 10.11809/bqzbgcxb2021.10.003.FENG Bodi, YANG Haitao, LI Gaoyuan, et al. Research summary of convolutional neural network in SAR image target recognition[J]. Journal of Ordnance Equipment Engineering, 2021, 42(10): 15–22. doi: 10.11809/bqzbgcxb2021.10.003. [2] 黄钟泠, 姚西文, 韩军伟. 面向SAR图像解译的物理可解释深度学习技术进展与探讨[J]. 雷达学报, 2021, 11(1): 107–125. doi: 10.12000/JR21165.HUANG Zhongling, YAO Xiwen, and HAN Junwei. Progress and perspective on physically explainable deep learning for synthetic aperture radar image interpretation[J]. Journal of Radars, 2022, 11(1): 107–125. doi: 10.12000/JR21165 [3] NOVAK L M, OWIRKA G J, and NETISHEN C M. Radar target identification using spatial matched filters[J]. Pattern Recognition, 1994, 27(4): 607–617. doi: 10.1016/0031-3203(94)90040-x. [4] 董刚刚. 基于单演信号的SAR图像目标识别技术研究[D]. [博士论文], 国防科学技术大学, 2016.DONG Ganggang. Study on target recognition in SAR imagevia the monogenic signal[D]. [Ph. D. dissertation], National University of Defense Technology, 2016. [5] SISTERSON L K, DELANEY J R, GRAVINA S J, et al. An architecture for semi-automated radar image exploitation[J]. Lincoln Laboratory Journal, 1998, 11(2): 175–204. [6] MORRISON D P, ECKERT JR A C, and SHIELDS F J. Studies of advanced detection technology sensor (ADTS) data[C]. SPIE 2230, Algorithms for Synthetic Aperture Radar Imagery, Orlando, USA, 1994: 370–378. doi: 10.1117/12.177185. [7] ROSS T D, WORRELL S W, VELTEN V J, et al. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery, Orlando, USA, 1998: 566–573. doi: 10.1117/12.321859. [8] RESSLER M B, WILLIAMS R L, GROSS D C, et al. Bayesian multiple-look updating applied to the SHARP ATR system[C]. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, Orlando, USA, 2000: 418–427. doi: 10.1117/12.396354. [9] 丁军, 刘宏伟, 王英华, 等. 一种联合阴影和目标区域图像的SAR目标识别方法[J]. 电子与信息学报, 2015, 37(3): 594–600. doi: 10.11999/JEIT140713.DING Jun, LIU Hongwei, WANG Yinghua, et al. SAR target recognition by combining images of the shadow region and target region[J]. Journal of Electronics & Information Technology, 2015, 37(3): 594–600. doi: 10.11999/JEIT140713. [10] RUSSAKOVSKY O, DENG Jia, SU Hao, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211–252. doi: 10.1007/s11263-015-0816-y. [11] CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720. [12] 杜兰, 王兆成, 王燕, 等. 复杂场景下单通道SAR目标检测及鉴别研究进展综述[J]. 雷达学报, 2020, 9(1): 34–54. doi: 10.12000/JR19104.DU Lan, WANG Zhaocheng, WANG Yan, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes[J]. Journal of Radars, 2020, 9(1): 34–54. doi: 10.12000/JR19104. [13] 郁文贤. 自动目标识别的工程视角述评[J]. 雷达学报, 2022, 11(5): 737–752. doi: 10.12000/JR22178.YU Wenxian. Automatic target recognition from an engineering perspective[J]. Journal of Radars, 2022, 11(5): 737–752. doi: 10.12000/JR22178. [14] KECHAGIAS-STAMATIS O and AOUF N. Automatic target recognition on synthetic aperture radar imagery: A survey[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(3): 56–81. doi: 10.1109/MAES.2021.3049857. [15] LI Jianwei, YU Zhentao, YU Lu, et al. A comprehensive survey on SAR ATR in deep-learning era[J]. Remote Sensing, 2023, 15(5): 1454. doi: 10.3390/rs15051454. [16] KEYDEL E R, LEE S W, and MOORE J T. MSTAR extended operating conditions: A tutorial[C]. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, USA, 1996: 228–242. doi: 10.1117/12.242059. [17] 王璇. 分辨率与SAR目标检测分类性能的关联性研究[D]. [硕士论文], 电子科技大学, 2012.WANG Xuan. Research on the correlation between resolution and SAR target detection and classification performance[D]. [Master dissertation], University of Electronic Science and Technology of China, 2012. [18] CASTEEL JR C H, GORHAM L A, MINARDI M J, et al. A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment[C]. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, Orlando, USA, 2007: 97–103. doi: 10.1117/12.731457. [19] ERTIN E, AUSTIN C D, SHARMA S, et al. GOTCHA experience report: Three-dimensional SAR imaging with complete circular apertures[C]. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, Orlando, USA, 2007: 9–20. doi: 10.1117/12.723245. [20] 朱岱寅, 耿哲, 俞翔, 等. 地面目标多角度SAR数据集构建与目标识别方法[J]. 南京航空航天大学学报, 2022, 54(5): 985–994. doi: 10.16356/j.1005-2615.2022.05.022.ZHU Daiyin, GENG Zhe, YU Xiang, et al. SAR database construction for ground targets at multiple angles and target recognition method[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2022, 54(5): 985–994. doi: 10.16356/j.1005-2615.2022.05.022. [21] SUN Xian, LV Yixuan, WANG Zhirui, et al. Scan: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226517. doi: 10.1109/TGRS.2022.3166174. [22] HUANG Lanqing, LIU Bin, LI Boying, et al. Opensarship: A dataset dedicated to sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672. [23] LI Boying, LIU Bin, HUANG Lanqing, et al. OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in sentinel-1 imagery[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017: 1–5. doi: 10.1109/BIGSARDATA.2017.8124929. [24] HOU Xiyue, AO Wei, SONG Qian, et al. FUSAR-ship: Building a high-resolution SAR-AIS matchup dataset of gaofen-3 for ship detection and recognition[J]. Science China Information Sciences, 2020, 63(4): 140303. doi: 10.1007/s11432-019-2772-5. [25] MALMGREN-HANSEN D, KUSK A, DALL J, et al. Improving SAR automatic target recognition models with transfer learning from simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1484–1488. doi: 10.1109/LGRS.2017.2717486. [26] LEWIS B, SCARNATI T, SUDKAMP E, et al. A SAR dataset for ATR development: The synthetic and measured paired labeled experiment (SAMPLE)[C]. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, Baltimore, USA, 2019: 39–54. doi: 10.1117/12.2523460. [27] ZHANG Jinsong, XING Mengdao, and XIE Yiyuan. FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2174–2187. doi: 10.1109/TGRS.2020.3003264. [28] ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. HOG-ShipCLSNet: A novel deep learning network with HOG feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210322. doi: 10.1109/TGRS.2021.3082759. [29] WAGNER S A. SAR ATR by a combination of convolutional neural network and support vector machines[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 2861–2872. doi: 10.1109/TAES.2016.160061. [30] 王汝意, 张汉卿, 韩冰, 等. 基于角度内插仿真的飞机目标多角度SAR数据集构建方法研究[J]. 雷达学报, 2022, 11(4): 637–651. doi: 10.12000/jr21193.WANG Ruyi, ZHANG Hanqing, HAN Bing, et al. Multiangle SAR dataset construction of aircraft targets based on angle interpolation simulation[J]. Journal of Radars, 2022, 11(4): 637–651. doi: 10.12000/JR21193. doi: 10.12000/jr21193. [31] LIU Lei, PAN Zongxu, QIU Xiaolan, et al. SAR target classification with CycleGAN transferred simulated samples[C]. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 4411–4414. doi: 10.1109/IGARSS.2018.8517866. [32] HE Qishan, ZHAO Lingjun, JI Kefeng, et al. SAR target recognition based on task-driven domain adaptation using simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4019205. doi: 10.1109/LGRS.2021.3116707. [33] CHEN Zhuo, ZHAO Lingjun, HE Qishan, et al. Pixel-level and feature-level domain adaptation for heterogeneous SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4515205. doi: 10.1109/LGRS.2022.3214750. [34] SHI Yu, DU Lan, GUO Yuchen, et al. Unsupervised domain adaptation based on progressive transfer for ship detection: From optical to SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5230317. doi: 10.1109/TGRS.2022.3185298. [35] 化盈盈, 张岱墀, 葛仕明. 深度学习模型可解释性的研究进展[J]. 信息安全学报, 2020, 5(3): 1–12. doi: 10.19363/J.cnki.cn10-1380/tn.2020.05.01.HUA Yingying, ZHANG Daichi, and GE Shiming. Research progress in the interpretability of deep learning models[J]. Journal of Cyber Security, 2020, 5(3): 1–12. doi: 10.19363/J.cnki.cn10-1380/tn.2020.05.01. [36] 徐丰, 金亚秋. 微波视觉与SAR图像智能解译[J]. 雷达学报, 2024, 13(2): 285–306. doi: 10.12000/JR23225.XU Feng and JIN Yaqiu. Microwave vision and intelligent perception of radar imagery[J]. Journal of Radars, 2024, 13(2): 285–306. doi: 10.12000/JR23225. [37] XU Feng and ZHANG Xu. On the concept of semantic electromagnetics[C]. 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022: 1–3. doi: 10.1109/ACES-China56081.2022.10065038. [38] ZHANG Tianwen and ZHANG Xiaoling. Injection of traditional hand-crafted features into modern CNN-based models for SAR ship classification: What, why, where, and how[J]. Remote Sensing, 2021, 13(11): 2091. doi: 10.3390/rs13112091. [39] HUANG Zhongling, YAO Xiwen, LIU Ying, et al. Physically explainable CNN for SAR image classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 190: 25–37. doi: 10.1016/j.isprsjprs.2022.05.008. [40] POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098. [41] DING Baiyuan, WEN Gongjian, HUANG Xiaohong, et al. Target recognition in synthetic aperture radar images via matching of attributed scattering centers[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7): 3334–3347. doi: 10.1109/JSTARS.2017.2671919. [42] DING Baiyuan, WEN Gongjian, HUANG Xiaohong, et al. Data augmentation by multilevel reconstruction using attributed scattering center for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 979–983. doi: 10.1109/LGRS.2017.2692386. [43] WU Min, XING Mengdao, ZHANG Lei, et al. Super-resolution imaging algorithm based on attributed scattering center model[C]. 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi’an, China, 2014: 271–275. doi: 10.1109/ChinaSIP.2014.6889246. [44] LIU Hongwei, JIU Bo, LI Fei, et al. Attributed scattering center extraction algorithm based on sparse representation with dictionary refinement[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(5): 2604–2614. doi: 10.1109/TAP.2017.2673764. [45] ZHOU Yu, LI Yi, XIE Weitong, et al. A convolutional neural network combined with attributed scattering centers for SAR ATR[J]. Remote Sensing, 2021, 13(24): 5121. doi: 10.3390/rs13245121. [46] LIU Zhunga, WANG Longfei, WEN Zaidao, et al. Multilevel scattering center and deep feature fusion learning framework for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5227914. doi: 10.1109/TGRS.2022.3174703. [47] FENG Sijia, JI Kefeng, ZHANG Linbin, et al. SAR target classification based on integration of asc parts model and deep learning algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 10213–10225. doi: 10.1109/JSTARS.2021.3116979. [48] FENG Sijia, JI Kefeng, WANG Fulai, et al. PAN: Part attention network integrating electromagnetic characteristics for interpretable SAR vehicle target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5204617. doi: 10.1109/TGRS.2023.3256399. [49] CHOI J H, LEE M J, JEONG N H, et al. Fusion of target and shadow regions for improved SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226217. doi: 10.1109/TGRS.2022.3165849. [50] LI Feng, YI Min, ZHANG Chaoqi, et al. POLSAR target recognition using a feature fusion framework based on monogenic signal and complex-valued nonlocal network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 7859–7872. doi: 10.1109/JSTARS.2022.3194551. [51] LI Feng, ZHANG Chaoqi, ZHANG Xin, et al. MF-DCMANet: A multi-feature dual-stage cross manifold attention network for PolSAR target recognition[J]. Remote Sensing, 2023, 15(9): 2292. doi: 10.3390/rs15092292. [52] LANG Haitao, WU Siwen, and XU Yongjie. Ship classification in SAR images improved by AIS knowledge transfer[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(3): 439–443. doi: 10.1109/LGRS.2018.2792683. [53] XING Xiangwei, JI Kefeng, ZOU Huanxin, et al. Ship classification in TerraSAR-X images with feature space based sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1562–1566. doi: 10.1109/LGRS.2013.2262073. [54] MARGARIT G and TABASCO A. Ship classification in single-pol SAR images based on fuzzy logic[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8): 3129–3138. doi: 10.1109/TGRS.2011.2112371. [55] 吕艺璇, 王智睿, 王佩瑾, 等. 基于散射信息和元学习的SAR图像飞机目标识别[J]. 雷达学报, 2022, 11(4): 652–665. doi: 10.12000/JR22044.LYU Yixuan, WANG Zhirui, WANG Peijin et al. Scattering information and meta-learning based SAR images interpretation for aircraft target recognition[J]. Journal of Radars, 2022, 11(4): 652–665. doi: 10.12000/JR22044. [56] KANG Yuzhuo, WANG Zhirui, ZUO Haoyu, et al. ST-Net: Scattering topology network for aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5202117. doi: 10.1109/TGRS.2023.3236987. [57] KARPATNE A, ATLURI G, FAGHMOUS J H, et al. Theory-guided data science: A new paradigm for scientific discovery from data[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2318–2331. doi: 10.1109/TKDE.2017.2720168. [58] HUANG Zhongling, DATCU M, PAN Zongxu, et al. Deep SAR-Net: Learning objects from signals[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 179–193. doi: 10.1016/j.isprsjprs.2020.01.016. [59] HUANG Zhongling, DATCU M, PAN Zongxu, et al. A hybrid and explainable deep learning framework for SAR images[C]. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, USA, 2020: 1727–1730. doi: 10.1109/IGARSS39084.2020.9323845. [60] HUANG Zhongling, DUMITRU C O, and REN Jun. Physics-aware feature learning of sar images with deep neural networks: A case study[C]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021: 1264–1267. doi: 10.1109/IGARSS47720.2021.9554842. [61] LEE J S, GRUNES M R, AINSWORTH T L, et al. Unsupervised classification using polarimetric decomposition and the complex wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249–2258. doi: 10.1109/36.789621. [62] LIU Jiaming, XING Mengdao, YU Hanwen, et al. EFTL: Complex convolutional networks with electromagnetic feature transfer learning for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5209811. doi: 10.1109/TGRS.2021.3083261. [63] FENG Sijia, JI Kefeng, MA Xiaojie, et al. Target region segmentation in SAR vehicle chip image with ACM net[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4014605. doi: 10.1109/LGRS.2021.3085188. [64] FENG Sijia, JI Kefeng, WANG Fulai, et al. Electromagnetic scattering feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5235415. doi: 10.1109/TGRS.2022.3208333. [65] HUANG Zhongling, WU Chong, YAO Xiwen, et al. Physics inspired hybrid attention for SAR target recognition[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 207: 164–174. doi: 10.1016/j.isprsjprs.2023.12.004. [66] 黄钟泠, 吴冲, 姚西文, 等. 基于时频分析的SAR目标微波视觉特性智能感知方法与应用[J]. 雷达学报, 2024, 13(2): 331–344. doi: 10.12000/jr23191.HUANG Zhongling, WU Chong, YAO Xiwen et al. Physically explainable intelligent perception and application of SAR target characteristics based on time-frequency analysis[J]. Journal of Radars, 2024, 13(2): 331–344. doi: 10.12000/JR23191. doi: 10.12000/jr23191. [67] TAI T, TODA M, SENZAKI K, et al. Leveraging physics-guided features for domain adaptation in SAR target classification[C]. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023: 6001–6004. doi: 10.1109/IGARSS52108.2023.10283259. [68] DING Jun, CHEN Bo, LIU Hongwei, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368. doi: 10.1109/LGRS.2015.2513754. [69] GUO Jiayi, LEI Bin, DING Chibiao, et al. Synthetic aperture radar image synthesis by using generative adversarial nets[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1111–1115. doi: 10.1109/LGRS.2017.2699196. [70] ZHANG Mingrui, CUI Zongyong, WANG Xianyuan, et al. Data augmentation method of SAR image dataset[C]. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 5292–5295. doi: 10.1109/IGARSS.2018.8518825. [71] 张明蕊. SAR图像数据分集与扩容方法研究[D]. [硕士论文], 电子科技大学, 2019.ZHANG Mingrui. Research of SAR image data diversity and data augmentation method[D]. [Master dissertation], University of Electronic Science and Technology of China, 2019. [72] DING Baiyuan and WEN Gongjian. Target recognition of SAR images based on multi-resolution representation[J]. Remote Sensing Letters, 2017, 8(11): 1006–1014. doi: 10.1080/2150704X.2017.1346397. [73] YAN Yue. Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition[J]. Journal of Electronic Imaging, 2018, 27(2): 023024. doi: 10.1117/1.JEI.27.2.023024. [74] WANG Ruonan, WANG Zhaocheng, XIA Kewen, et al. Target recognition in single-channel SAR images based on the complex-valued convolutional neural network with data augmentation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(2): 796–804. doi: 10.1109/TAES.2022.3190804. [75] DOO S H, SMITH G, and BAKER C. Target classification performance as a function of measurement uncertainty[C]. 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore, 2015: 587–590. doi: 10.1109/APSAR.2015.7306277. [76] KWAK Y, SONG W J, and KIM S E. Speckle-noise-invariant convolutional neural network for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4): 549–553. doi: 10.1109/LGRS.2018.2877599. [77] YANG Minjia, BAI Xueru, WANG Li, et al. Mixed loss graph attention network for few-shot SAR target classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5216613. doi: 10.1109/TGRS.2021.3124336. [78] LI Weijie, YANG Wei, ZHANG Wenpeng, et al. Hierarchical disentanglement-alignment network for robust SAR vehicle recognition[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 9661–9679. doi: 10.1109/JSTARS.2023.3324182. [79] DING Baiyuan and WEN Gongjian. Exploiting multi-view SAR images for robust target recognition[J]. Remote Sensing, 2017, 9(11): 1150. doi: 10.3390/rs9111150. [80] LV Junya and LIU Yue. Data augmentation based on attributed scattering centers to train robust CNN for SAR ATR[J]. IEEE Access, 2019, 7: 25459–25473. doi: 10.1109/ACCESS.2019.2900522. [81] CRESWELL A, WHITE T, DUMOULIN V, et al. Generative adversarial networks: An overview[J]. IEEE Signal Processing Magazine, 2018, 35(1): 53–65. doi: 10.1109/MSP.2017.2765202. [82] AUER S, BAMLER R, and REINARTZ P. RaySAR-3D SAR simulator: Now open source[C]. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 6730–6733. doi: 10.1109/IGARSS.2016.7730757. [83] GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750. [84] BEN-DAVID S, BLITZER J, CRAMMER K, et al. Analysis of representations for domain adaptation[M]. SCHÖLKOPF B, PLATT J, and HOFMANN T. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Cambridge: The MIT Press, 2007, 19: 137–144. doi: 10.7551/mitpress/7503.003.0022. [85] MORENO-TORRES J G, RAEDER T, ALAIZ-RODRíGUEZ R, et al. A unifying view on dataset shift in classification[J]. Pattern Recognition, 2012, 45(1): 521–530. doi: 10.1016/j.patcog.2011.06.019. [86] BEN-DAVID S, BLITZER J, CRAMMER K, et al. A theory of learning from different domains[J]. Machine Learning, 2010, 79(1/2): 151–175. doi: 10.1007/s10994-009-5152-4. [87] LONG Mingsheng, CAO Yue, CAO Zhangjie, et al. Transferable representation learning with deep adaptation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(12): 3071–3085. doi: 10.1109/TPAMI.2018.2868685. [88] SUN Baochen and SAENKO K. Deep CORAL: Correlation alignment for deep domain adaptation[C]. 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 443–450. doi: 10.1007/978-3-319-49409-8_35. [89] ZELLINGER W, GRUBINGER T, LUGHOFER E, et al. Central moment discrepancy (CMD) for domain-invariant representation learning[C]. 5th International Conference on Learning Representations, Toulon, France, 2017. [90] GANIN Y and LEMPITSKY V. Unsupervised domain adaptation by backpropagation[C]. The 32nd International Conference on Machine Learning, Lile, France, 2015: 1180–1189. [91] SHEN Jian, QU Yanru, ZHANG Weinan, et al. Wasserstein distance guided representation learning for domain adaptation[C]. The 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 4058–4065. doi: 10.1609/aaai.v32i1.11784. [92] GHIFARY M, KLEIJN W B, ZHANG Mengjie, et al. Domain generalization for object recognition with multi-task autoencoders[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 2551–2559. doi: 10.1109/iccv.2015.293. [93] LI Da, ZHANG Jianshu, YANG Yongxin, et al. Episodic training for domain generalization[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 1446–1455. doi: 10.1109/iccv.2019.00153. [94] ZHOU Kaiyang, YANG Yongxin, CAVALLARO A, et al. Learning generalisable omni-scale representations for person re-identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5056–5069. doi: 10.1109/TPAMI.2021.3069237. [95] SHAO Rui, LAN Xiangyuan, LI Jiawei, et al. Multi-adversarial discriminative deep domain generalization for face presentation attack detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 10015–10023. doi: 10.1109/CVPR.2019.01026. [96] WANG Zhen, WANG Qiansheng, LV Chengguo, et al. Unseen target stance detection with adversarial domain generalization[C]. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020: 1–8. doi: 10.1109/IJCNN48605.2020.9206635. [97] 李理, 孙玉林, 曹然, 等. 基于联合分布适配的水下声源测距算法研究[J]. 电子与信息学报, 2022, 44(6): 2061–2070. doi: 10.11999/JEIT211418.LI Li, SUN Yulin, CAO Ran, et al. Research on underwater source ranging algorithm based on joint distribution adaptation[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2061–2070. doi: 10.11999/JEIT211418. [98] 范苍宁, 刘鹏, 肖婷, 等. 深度域适应综述: 一般情况与复杂情况[J]. 自动化学报, 2021, 47(3): 515–548. doi: 10.16383/j.aas.c200238.FAN Cangning, LIU Peng, XIAO Ting, et al. A review of deep domain adaptation: General situation and complex situation[J]. Acta Automatica Sinica, 2021, 47(3): 515–548. doi: 10.16383/j.aas.c200238. [99] ZHANG Lei and GAO Xinbo. Transfer adaptation learning: A decade survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(1): 23–44. doi: 10.1109/TNNLS.2022.3183326. [100] ZHANG Wei, ZHU Yongfeng, and FU Qiang. Adversarial deep domain adaptation for multi-band SAR images classification[J]. IEEE Access, 2019, 7: 78571–78583. doi: 10.1109/ACCESS.2019.2922844. [101] ZHANG Yukun, GUO Xiansheng, LEUNG H, et al. Transfer learning with shared and specific structures for SAR target recognition[C]. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 1003–1006. doi: 10.1109/IGARSS46834.2022.9883216. [102] CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, 2020: 1597–1607. [103] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. Active learning SAR image classification method crossing different imaging platforms[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4514105. doi: 10.1109/LGRS.2022.3208468. [104] ZHAO Siyuan, XU Yin, LUO Ying, et al. A domain adaptation network for cross-imaging satellites sar image ship classification[C]. IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 1580–1583. doi: 10.1109/IGARSS46834.2022.9883273. [105] ZHAO Siyuan, ZHANG Zenghui, ZHANG Tao, et al. Transferable SAR image classification crossing different satellites under open set condition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4506005. doi: 10.1109/LGRS.2022.3159179. [106] GU Xiang, SUN Jian, and XU Zongben. Spherical space domain adaptation with robust pseudo-label loss[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9098–9107. doi: 10.1109/CVPR42600.2020.00912. [107] GAO Zhiqiang, ZHANG Shufei, HUANG Kaizhu, et al. Gradient distribution alignment certificates better adversarial domain adaptation[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 8917–8926. doi: 10.1109/ICCV48922.2021.00881. [108] ZOU Bin, QIN Jiang, and ZHANG Lamei. Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution SAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 173–190. doi: 10.1016/j.isprsjprs.2023.05.009. [109] SHI Yu, DU Lan, and GUO Yuchen. Unsupervised domain adaptation for SAR target detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6372–6385. doi: 10.1109/JSTARS.2021.3089238. [110] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. A feature decomposition-based method for automatic ship detection crossing different satellite SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5234015. doi: 10.1109/TGRS.2022.3201628. [111] ZHAO Siyuan, ZHANG Zenghui, GUO Weiwei, et al. An automatic ship detection method adapting to different satellites SAR images with feature alignment and compensation loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225217. doi: 10.1109/TGRS.2022.3160727. [112] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 198: 16–29. doi: 10.1016/j.isprsjprs.2023.02.011. [113] ROSTAMI M, KOLOURI S, EATON E, et al. Deep transfer learning for few-shot SAR image classification[J]. Remote Sensing, 2019, 11(11): 1374. doi: 10.3390/rs11111374. [114] SONG Yucheng, LI Jingrun, GAO Peng, et al. Two-stage cross-modality transfer learning method for military-civilian SAR ship recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4506405. doi: 10.1109/LGRS.2022.3162707. [115] ZHAO Shuangmei and LANG Haitao. Improving deep subdomain adaptation by dual-branch network embedding attention module for SAR ship classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8038–8048. doi: 10.1109/JSTARS.2022.3206753. [116] GUO Yuchen, DU Lan, and LYU Guoxin. SAR target detection based on domain adaptive faster R-CNN with small training data size[J]. Remote Sensing, 2021, 13(21): 4202. doi: 10.3390/rs13214202. [117] ZHANG Jun, LI Simin, DONG Yongfeng, et al. Hierarchical similarity alignment for domain adaptive ship detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5240611. doi: 10.1109/TGRS.2022.3227626. [118] ZHANG Yukun, GUO Xiansheng, LI Lin, et al. Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition[J]. Pattern Recognition, 2022, 126: 108590. doi: 10.1016/j.patcog.2022.108590. [119] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031. [120] LEI Zhengxin, XU Feng, WEI Jiangtao, et al. SAR-NeRF: Neural radiance fields for synthetic aperture radar multi-view representation[EB/OL]. https://arxiv.org/abs/2307.05087, 2023. [121] FU Shilei and XU Feng. Differentiable SAR renderer and image-based target reconstruction[J]. IEEE Transactions on Image Processing, 2022, 31: 6679–6693. doi: 10.1109/TIP.2022.3215069. [122] 仇晓兰, 焦泽坤, 杨振礼, 等. 微波视觉三维SAR关键技术及实验系统初步进展[J]. 雷达学报, 2022, 11(1): 1–19. doi: 10.12000/JR22027.QIU Xiaolan, JIAO Zekun, YANG Zhenli, et al. Key technology and preliminary progress of microwave vision 3D SAR experimental system[J]. Journal of Radars, 2022, 11(1): 1–19. doi: 10.12000/JR22027. [123] CHANG Yupeng, WANG Xu, WANG Jindong, et al. A survey on evaluation of large language models[J]. ACM Transactions on Intelligent Systems and Technology, 2024, 15(3): 39. doi: 10.1145/3641289. [124] KIRILLOV A, MINTUN E, RAVI N, et al. Segment anything[C]. 2023 IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 3992–4003. doi: 10.1109/ICCV51070.2023.00371. [125] WOOLLARD M, BLACKNELL D, GRIFFITHS H, et al. SARCASTIC v2.0—high-performance SAR simulation for next-generation ATR systems[J]. Remote Sensing, 2022, 14(11): 2561. doi: 10.3390/rs14112561. [126] 董纯柱, 胡利平, 朱国庆, 等. 地面车辆目标高质量SAR图像快速仿真方法[J]. 雷达学报, 2015, 4(3): 351–360. doi: 10.12000/JR15057.DONG Chunzhu, HU Liping, ZHU Guoqing et al. Efficient simulation method for high quality SAR images of complex ground vehicles[J]. Journal of Radars, 2015, 4(3): 351–360. doi: 10.12000/JR15057. [127] NIU Shengren, QIU Xiaolan, LEI Bin, et al. A SAR target image simulation method with DNN embedded to calculate electromagnetic reflection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2593–2610. doi: 10.1109/JSTARS.2021.3056920. [128] WEI Jiangtao, LUOMEI Yixiang, ZHANG Xu, et al. Learning surface scattering parameters from SAR images using differentiable ray tracing[EB/OL]. https://arxiv.org/abs/2401.01175, 2024. [129] LV Xiaoling, QIU Xiaolan, YU Wenming, et al. Simulation-aided SAR target classification via dual-branch reconstruction and subdomain alignment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5214414. doi: 10.1109/TGRS.2023.3305094. [130] SHI Yu, DU Lan, LI Chen, et al. Unsupervised domain adaptation for SAR target classification based on domain- and class-level alignment: From simulated to real data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 207: 1–13. doi: 10.1016/j.isprsjprs.2023.11.010. [131] GUO Qian, XU Huilin, and XU Feng. Causal adversarial autoencoder for disentangled SAR image representation and few-shot target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5221114. doi: 10.1109/TGRS.2023.3330478. [132] LI Weijie, YANG Wei, LIU Li, et al. Discovering and explaining the noncausality of deep learning in SAR ATR[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 4004605. doi: 10.1109/LGRS.2023.3266493. [133] LIU Jiaxiang, LIU Zhunga, ZHANG Zuowei, et al. A new causal inference framework for SAR target recognition[J]. IEEE Transactions on Artificial Intelligence, 2024: 1–15. doi: 10.1109/TAI.2024.3357664. 期刊类型引用(7)
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