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Multichannel False-target Discrimination in SAR Images Based on Sub-aperture and Full-aperture Feature Learning
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摘要: SAR多通道引起的虚假目标与散焦的船舶目标形状纹理特征非常相似，在全孔径SAR图像中难以区分。针对此类虚假目标造成的虚警问题，该文提出一种基于子孔径与全孔径特征学习的SAR多通道虚假目标鉴别方法。首先，对复数SAR图像进行幅值计算得到幅度图像，利用迁移学习方法提取幅度图像中的全孔径特征；接着，对复数SAR图像进行子孔径分解获得一系列子孔径图像，然后用栈式卷积自编码器(SCAE)提取子孔径图像中的子孔径特征；最后，将子孔径和全孔径特征进行串联并利用联合特征进行分类。在高分三号超精细条带模式SAR图像上的实验结果表明，该方法可以有效的鉴别船舶目标和多通道虚假目标，与仅使用全孔径特征学习的方法相比准确率提升了16.32%。Abstract: False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.
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图 1 GF-3 UFS图像中的多通道虚假目标示意图
Figure 1. An example of multichannel false-target in a GF-3 UFS SAR image
图 4 真实船舶目标、纯海面、多通道虚假目标切片的全孔径图像及其对应子孔径图像
Figure 4. Examples of sublook amplitude for ships (a)—(d), sea (e), multichannel false-targets (f)—(h) and the relevant amplitude SLC images
图 9 不同程度散焦图像及其对应方位误差曲线图
Figure 9. Different degrees of defocused images and their corresponding azimuth error curves
图 11 t-SNE降维特征可视化结果比较
Figure 11. Comparison of FFL and SFFL visualization results using t-SNE
图 12 纹理特征与真实目标相似的多通道虚假目标切片鉴别结果详细说明
Figure 12. The elaborate explanation of the discrimination results of multichannel false-targets similar to real targets in texture
图 14 不同子孔径数目下SFFL方法的准确率
Figure 14. Overall accuracy of SFFL method with different numbers of sub-apertures
表 1 各个自编码单元的卷积层设计
Table 1. Design of convolutional layers in each auto-encoder unit
自编码单元 通道数 卷积核尺寸 1 32 5×5 2 64 5×5 3 64 3×3 4 128 4×4 
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表 2 基于子孔径与全孔径特征学习的算法
Table 2. SFFL algorithm
输入：复数SAR图像$ C\left(x,y\right) $ for 所有训练样本$ C\left(x,y\right) $ do： (1) 通过子孔径分解获得子孔径图像$ S\left(x,y\right) $ (2) 训练栈式卷积自编码网络$ {F}_{2} $，获得子孔径特征${\rm{\varphi } }_{2}\left(x, y\right)$ for 子孔径图像$ S\left(x,y\right) $ do： for 所有自编码单元$ l $ do： 计算自编码单元的输出
${y}_{i}^{l-1}={F}_{2}\left(\displaystyle\sum\limits_{i=1}^{ {M}_{l-1} }{x}_{i}^{l-1},{\theta }_{2}\right)$计算损失函数
${\rm{Loss} } = \displaystyle\sum \limits_{i = 1}^{ {M_{l - 1} } } \left\| {x_i^{l - 1} - y_i^{l - 1} } \right\|_{\rm{F}}^2$反向传播，更新参数θ2 end for end for (3) 计算全孔径幅度图像$ I\left(x,y\right)=\sqrt{{A\left(x,y\right)}^{2}+{B\left(x,y\right)}^{2}} $ (4) 迁移学习ResNet-18预训练模型$ {F}_{1} $，微调参数θ1，获得
全孔径特征${\rm{\varphi } }_{1}\left(x, y\right)$(5) 特征归一化${\rm{\varphi } }_{1}\left(x, y\right)$, ${\rm{\varphi } }_{2}\left(x, y\right)$ (6) 特征拼接得到${\rm{\psi } }\left(x, y\right)$ (7) 计算损失函数(交叉熵) (8) 反向传播，更新参数θ end for 输出：最终模型
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表 3 GF-3超精细条带图像参数
Table 3. The detailed information of GF-3 UFS SAR images used in the experiment
参数 图像1—图像8 成像模式 UFS 产品类型 SLC 产品级别 L1A级 轨道模式 升轨 极化方式 DH 斜距分辨率(m) 2.5～5.0 方位向分辨率(m) 3 幅宽(km) 30 像元间距[Rg×Az](m) 1.124×1.729 入射角(º) 39.54～41.52
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表 4 二分类问题混淆矩阵
Table 4. Confusion matrix of binary classification
预测结果 实际为真 实际为假 预测为真 TP FP 预测为假 FN TN
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表 5 不同方法的鉴别性能对比
Table 5. Comparison of discrimination performance of different methods
实验方法 准确率(%) 运行时间(s) SLCF+SVM 90.56 0.8823 SFL 90.99 4.9824 FFL 83.69 4.2189 SFFL 96.57 5.3546
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表 6 不同方法的混淆矩阵
Table 6. Confusion matrix of different methods
方法 预测结果 识别结果实际为真 识别结果实际为假 SLCF+SVM 预测为真 70 20 预测为假 2 141 SFL 预测为真 68 17 预测为假 4 144 FFL 预测为真 69 35 预测为假 3 126 SFFL 预测为真 69 5 预测为假 3 156
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表 7 加入散焦数据结果对比(%)
Table 7. Comparison of two methods after adding defocus data (%)
测试数据 FFL方法准确率 SFFL方法准确率 GF-3数据 83.69 96.57 GF-3数据+仿真散焦数据 80.41 96.73
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