一种基于特征交汇关键点检测和Sim-CSPNet的SAR图像配准算法

项德良; 徐益豪; 程建达; 胡粲彬; 孙晓坤

doi:10.12000/JR22110

一种基于特征交汇关键点检测和Sim-CSPNet的SAR图像配准算法

DOI: 10.12000/JR22110

项德良^{1, 2},
徐益豪¹,
程建达^1, ,,
胡粲彬¹,
孙晓坤¹

1.
北京化工大学信息科学与技术学院北京 100029
2.
北京化工大学软物质科学与工程高精尖创新中心北京 100029

基金项目: 国家自然科学基金(62171015)

详细信息

作者简介:
项德良，博士，教授，主要研究方向为SAR/PolSAR信息处理、探地雷达等

徐益豪，硕士生，主要研究方向为人工智能、SAR图像配准

程建达，博士生，主要研究方向为极化SAR图像分类、目标检测、配准等

胡粲彬，博士，讲师，主要研究方向为SAR图像目标检测识别、极化SAR信息处理等

孙晓坤，博士，副研究员，主要研究方向为SAR信息处理、SAR图像质量评定及应用等

通讯作者:
程建达 cjd_buct@163.com

责任主编：仇晓兰 Corresponding Editor: QIU Xiaolan
中图分类号: TP75
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出版历程
- 收稿日期: 2022-06-08
- 修回日期: 2022-07-20
- 网络出版日期: 2022-08-03
- 刊出日期: 2022-12-28

An Algorithm Based on a Feature Interaction-based Keypoint Detector and Sim-CSPNet for SAR Image Registration

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2.
Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

Funds: The National Natural Science Foundation of China (62171015)

More Information

Corresponding author: CHENG Jianda, cjd_buct@163.com

摘要

摘要: 合成孔径雷达(SAR)图像存在固有的相干斑噪声和几何畸变，并且其成像过程中图像之间存在非线性辐射差异，因此SAR图像配准是近年来最具挑战性的任务之一。关键点的可重复性和特征描述符的有效性直接影响基于特征的配准方法精度。该文提出了一种新颖的基于特征交汇的关键点检测器，它包含3个并行的检测器，即相位一致性(PC)检测器、水平和垂直方向梯度检测器以及局部变异系数检测器。所提出的特征交汇关键点检测器不仅可以有效提取具有高重复性的关键点，而且大大减少了错误关键点的数量，从而降低了特征描述和匹配的计算成本。同时，该文设计了一种孪生跨阶段部分网络(Sim-CSPNet)来快速提取包含深层和浅层特征的特征描述符。与传统手工设计的浅层描述符相比，它可以用来获得更准确的匹配点对。通过对多组SAR图像进行配准实验，并与其他3种方法进行对比，验证了该方法具有很好的配准结果。
- SAR图像配准 /
- 局部变异系数 /
- 相位一致性 /
- 结构张量 /
- 密集孪生网络
Abstract: Synthetic Aperture Radar (SAR) image registration has recently been one of the most challenging tasks because of speckle noise, geometric distortion and nonlinear radiation differences between SAR images. The repeatability of keypoints and the effectiveness of feature descriptors directly affect the registration accuracy of feature-based methods. In this paper, we propose a novel Feature Intersection-based (FI) keypoint detector, which contains three parallel detectors, i.e., a Phase Congruency (PC) detector, horizontal/vertical oriented gradient detectors, and a Local Coefficient of Variation (LCoV) detector. The proposed FI detector can effectively extract keypoints with high repeatabilityand greatly reduce the number of false keypoints, thus greatly reducing the computational cost of feature description and matching. We further propose the Siamese Cross Stage Partial Network (Sim-CSPNet) to rapidly extract feature descriptors containing deep and shallow features, which can obtain more correct matching point pairs than traditional synthetic shallow descriptors. Through the registration experiments on multiple sets of SAR images, the proposed method is verified to have better registration results than the three existing methods.
- SAR image registration /
- Local Coefficient of Variation (LCoV) /
- Phase Congruency (PC) /
- Structure tensor /
- Dense siamese network

HTML全文

图 1 本文算法总体流程图

Figure 1. Overall flow chart of the proposed algorithm

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图 2 基于特征交汇关键点检测器流程图

Figure 2. Flowchart of the feature intersection-based keypoint detector

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图 3 PC检测器在SAR图像对数变换前后的关键点检测结果

Figure 3. Keypoint detection results of PC detector before and after logarithmic transformation of SAR images

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图 4 不同关键点检测器得到的关键点检测结果

Figure 4. Keypoint detection results obtained by different keypoint detectors

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图 5 不同关键点检测器得到的关键点匹配结果

Figure 5. Keypoint matching results obtained by different keypoint detectors

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图 6 孪生跨阶段部分网络结构

Figure 6. Architecture of the siamese cross stage partial network

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图 7 4个不同场景的SAR实验数据

Figure 7. SAR experimental data of four different scenarios

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图 8 不同算法在图像对A的特征匹配结果

Figure 8. Feature matching results of pair A with different method

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图 9 不同算法在图像对B的特征匹配结果

Figure 9. Feature matching results of pair B with different method

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图 10 不同算法在图像对C的特征匹配结果

Figure 10. Feature matching results of pair C with different method

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图 11 不同算法在图像对D的特征匹配结果

Figure 11. Feature matching results of pair D with different method

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图 12 不同算法的棋盘格叠加图

Figure 12. Checkerboard overlays for different algorithms

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图 13 图像对A上不同关键点检测器的配准结果

Figure 13. Registration results with different keypoint detectors on pair A

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图 14 图像对B上不同关键点检测器的配准结果

Figure 14. Registration results with different keypoint detectors on pair B

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图 15 图像对C上不同关键点检测器的配准结果

Figure 15. Registration results with different keypoint detectors on pair C

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图 16 图像对D上不同关键点检测器的配准结果

Figure 16. Registration results with different keypoint detectors on pair D

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表 1 Sim-CSPNet模型结构

Table 1. Sim-CSPNet model structure

网络模块	网络层	输出尺寸
Input layer	Input	64×64×1
Conv layer	Conv(3×3), stride(2)	32×32×32
Block 1	Half of previous layer	32×32×16
	Conv(1×1), stride(1)	32×32×48
	Conv(3×3), stride(1)	32×32×12
	Connect	32×32×28
	Conv(1×1), stride(1)	32×32×48
	Conv(3×3), stride(1)	32×32×12
	Connect	32×32×40
	Conv(1×1), stride(1)	32×32×20
	Connect	32×32×36
Transition layer	Conv(1×1), stride(1)	32×32×18
Transition layer	Average pooling(2×2), stride(2)	16×16×18
Block 2		16×16×25
Transition layer	Conv(1×1), stride(1)	16×16×12
Transition layer	Average pooling(2×2), stride(2)	8×8×12
Block 3		8×8×21
Output layer	Conv(8×8), stride(1)	256×1

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表 2 实验使用的SAR图像对信息

Table 2. Information of SAR image pairs used in the experiment

传感器	图像对编号	图像大小	分辨率(m)	获取时间
GF-3	Pair A	1214×1130(左)	2×6	20200715(左)
	Pair A	1480×1207(右)	2×6	20200726(右)
	Pair B	1000×1000(左)	2×6	20200715(左)
	Pair B	1000×1000(右)	2×6	20200726(右)
Sentinel-1	Pair C	500×500(左)	11×14	20201010(左)
	Pair C	600×600(右)	11×14	20211222(右)
	Pair D	1374×1349(左)	11×14	20201010(左)
	Pair D	1597×1462(右)	11×14	20211222(右)

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表 3 不同方法在4对SAR图像上的比较

Table 3. Comparison of different methods on four pairs of SAR images

算法	Pair A			Pair B			Pair C			Pair D
算法	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)
SAR-SIFT	0.97	52	3215.9	0.89	51	1859.40	0.87	14	369.9	0.91	49	4232.1
KAZE-SAR	–	–	41.5	2.40	18	19.60	–	–	5.7	4.80	61	79.2
HardNet	1.13	11	158.9	–	–	63.50	0.80	21	28.9	0.96	65	146.2
本文方法	0.74	209	18.1	0.68	594	7.95	0.71	51	3.6	0.65	306	24.7

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表 4 不同关键点检测器的定量比较

Table 4. Quantitative comparison of different keypoint detectors

算法	指标	Pair A	Pair B	Pair C	Pair D
DoG	关键点数量(参考图像)	10651	3475	2992	13422
	关键点数量(待配准图像)	19288	3383	4246	19923
	时间(s)	14.9	6.64	3.56	21.28
	NCM	81	17	36	128
	RMSE	0.94	0.74	1.01	0.79
Harris	关键点数量(参考图像)	11513	8114	1867	14905
	关键点数量(待配准图像)	14209	6370	2494	15053
	时间(s)	12.47	6.79	3.39	13.32
	NCM	60	40	25	49
	RMSE	1.07	0.81	0.80	0.86
SAR-Harris	关键点数量(参考图像)	22746	9193	3391	23660
	关键点数量(待配准图像)	32693	10215	5584	37767
	时间(s)	49.4	11.31	7.6	34.68
	NCM	134	132	39	164
	RMSE	0.95	0.87	0.79	0.76
特征交汇检测器	关键点数量(参考图像)	7848	6133	752	10481
	关键点数量(待配准图像)	7961	5424	715	11499
	时间(s)	18.1	7.95	3.6	24.7
	NCM	209	594	51	306
	RMSE	0.74	0.68	0.71	0.65

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表 5 不同网络的定量比较

Table 5. Quantitative comparison of different networks

算法	Pair A			Pair B			Pair C			Pair D
算法	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)	RMSE	NCM	Time (s)
FI+L2Net	1.13	51	34.35	0.81	22	30.38	1.56	18	3.63	0.84	41	28.17
FI+HardNet	0.85	55	32.50	0.77	42	29.41	0.79	29	3.99	0.76	68	28.42
FI+SOSNet	0.83	93	14.21	0.70	79	9.24	0.86	48	2.77	0.92	121	11.70
本文方法	0.74	209	18.10	0.68	594	7.95	0.71	51	3.60	0.65	306	24.70

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