一种基于深度学习的SAR城市建筑区域叠掩精确检测方法

田野; 丁赤飚; 张福博; 石民安

doi:10.12000/JR23033

一种基于深度学习的SAR城市建筑区域叠掩精确检测方法

DOI: 10.12000/JR23033

田野^{1, 2, 3},
丁赤飚^{1, 2},
张福博^{1, 2, ,},
石民安^{1, 2, 3}

1.
中国科学院空天信息创新研究院微波成像技术国家级重点实验室北京 100190
2.
中国科学院空天信息创新研究院北京 100190
3.
中国科学院大学电子电气与通信工程学院北京 100049

基金项目: 国家重点研发计划(2021YFA0715404)

详细信息

作者简介:
田　野，博士生，主要研究方向为多通道SAR叠掩检测与深度学习

丁赤飚，博士，研究员，中国科学院院士，主要研究方向为合成孔径雷达、遥感信息处理和应用系统等

张福博，博士，副研究员，主要研究方向为SAR三维成像技术和高分辨率宽测绘带成像技术等

石民安，硕士生，主要研究方向为微波成像与人工智能

通讯作者:
张福博 zhangfb@aircas.ac.cn

责任主编：张群 Corresponding Editor: ZHANG Qun
中图分类号: TN957.52
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-11
- 修回日期: 2023-04-02
- 网络出版日期: 2023-04-24
- 刊出日期: 2023-04-28

SAR Building Area Layover Detection Based on Deep Learning

TIAN Ye^{1, 2, 3},
DING Chibiao^{1, 2},
ZHANG Fubo^{1, 2
, ,},
SHI Min’an^{1, 2, 3}

1.
National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: National Key R&D Program of China (2021YFA0715404)

More Information

Corresponding author: ZHANG Fubo, zhangfb@aircas.ac.cn

摘要

摘要: 建筑物叠掩检测在城市三维合成孔径雷达(3D SAR)成像流程中是至关重要的步骤，其不仅影响成像效率，还直接影响最终成像的质量。目前，用于建筑物叠掩检测的算法往往难以提取远距离全局空间特征，也未能充分挖掘多通道SAR数据中关于叠掩的丰富特征信息，导致现有叠掩检测算法的精确度无法满足城市3D SAR成像的要求。为此，该文结合Vision Transformer (ViT)模型和卷积神经网络(CNN)的优点，提出了一种基于深度学习的SAR城市建筑区域叠掩精确检测方法。ViT模型能够通过自注意力机制有效提取全局特征和远距离特征，同时CNN有着很强的局部特征提取能力。此外，该文所提方法还基于专家知识增加了用于挖掘通道间叠掩特征和干涉相位叠掩特征的模块，提高算法的准确率与鲁棒性，同时也能够有效地减轻模型在小样本数据集上的训练压力。最后在该文构建的机载阵列SAR数据集上测试，实验结果表明，该文所提算法检测准确率达到94%以上，显著高于其他叠掩检测算法。
- 深度学习 /
- 专家知识 /
- 3D SAR成像 /
- 建筑区域叠掩检测 /
- Vision Transformer模型
Abstract: Building layover detection is a crucial step in the 3D Synthetic Aperture Radar (SAR) imaging process in urban areas. It affects imaging efficiency and directly influences the final image quality. Currently, algorithms used for layover detection struggle to extract long-range global spatial characteristics and fail to fully exploit the rich features of layover in multi-channel SAR data. To address the issue of insufficient accuracy in existing layover detection algorithms to meet the requirements of urban 3D SAR imaging, this paper proposes a deep learning-powered SAR urban layover detection method that combines the advantages of the Vision Transformer (ViT) model and Convolutional Neural Network (CNN). The ViT model can efficiently extract global and long-range features through a self-attention mechanism, whereas the CNN has strong local feature extraction capabilities. Furthermore, the proposed method in this paper incorporates a module for investigating inter-channel layover features and interferometric phase layover features based on expert knowledge, which improves the accuracy and robustness of the algorithm while effectively decreasing the training pressure on the model in small-sample datasets. Finally, the proposed algorithm is tested on a self-built airborne array SAR dataset, and experimental findings revealed that the proposed algorithm achieves a detection accuracy of >94%, which is significantly higher than other layover detection algorithms, completely revealing the effectiveness of this method.
- Deep learning /
- Expert knowledge /
- 3D SAR imaging /
- Building area layover detection /
- Vision Transformer (ViT) model

HTML全文

图 1 城市区域SAR三维成像流程图

Figure 1. The flowchart of 3D SAR reconstruction of the urban area

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图 2 Transformer模块结构图

Figure 2. The structure of Transformer module

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图 3 本文提出的叠掩检测网络的结构示意图

Figure 3. The architecture diagram of layover detection network proposed in this paper

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图 4 ViT空间特征模块(ViT-SSFM)网络结构示意图

Figure 4. The network structure of the ViT-Spatial Structure Feature Module (ViT-SSFM)

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图 5 多通道特征模块流程示意图

Figure 5. The flowchart of multi-channel feature extraction module

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图 6 InSAR几何地理模型

Figure 6. The InSAR geometry model of layover

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图 7 干涉相位特征模块

Figure 7. Interference phase feature module

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图 8 数据集场景示意图

Figure 8. The illustration of a scene in the dataset

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图 9 数据集切片示意图

Figure 9. Image slices of dataset

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图 10 本文方法与传统方法的叠掩检测图

Figure 10. Layover detection of the proposed method and traditional methods

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图 11 不同深度学习方法的叠掩检测图

Figure 11. Layover detection of different deep learning methods

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图 12 不同训练数据量下的准确率

Figure 12. Accuracy with different proportion of training data

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表 1 机载SAR参数

Table 1. The parameters of airborne SAR

参数	数值
飞行高度	5 km
飞行速度	80 m/s
波段	Ku
入射角	40°
分辨率	0.3 m

下载: 导出CSV

表 2 本文方法与传统方法对比实验结果

Table 2. Comparison experiment results between the proposed method and traditional methods

实验方法	准确率	精准度	召回率	虚警率	漏警率
幅度法	0.7285	0.6041	0.5912	0.3959	0.4088
通道间FFT	0.7820	0.6295	0.8231	0.3705	0.1769
干涉相位法	0.6502	0.4506	0.4311	0.5494	0.5689
本文方法	0.9443	0.7619	0.8699	0.2380	0.1300

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表 3 本文方法与其他深度学习算法对比实验结果

Table 3. Comparison experiment results between the proposed method and other deep learning methods

实验方法	准确率	精准度	召回率	虚警率	漏警率	参数量(M)
UNet	0.8976	0.7463	0.8391	0.2537	0.1609	7.8
UNet++	0.8963	0.7481	0.8382	0.2519	0.1618	9.8
DeepLabV3	0.8614	0.7112	0.7933	0.2688	0.1767	15.3
DeepLabV3+	0.8831	0.7434	0.8291	0.2566	0.1709	15.6
ViT	0.8091	0.6331	0.6783	0.3668	0.3216	8.6
本文方法	0.9443	0.7619	0.8699	0.2380	0.1300	10.0

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表 4 消融实验结果

Table 4. Results of ablation experiments

ViT-SSFM	MCFM	IPFM	准确率	精准度	召回率
×	×	×	0.8891	0.7263	0.8173
√	×	×	0.9346	0.7512	0.8294
×	√	√	0.9162	0.7387	0.8516
√	√	√	0.9443	0.7619	0.8699

下载: 导出CSV

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