Cross-view geo-localization method based on multi-task joint learning

doi:10.11772/j.issn.1001-9081.2022040541

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1625-1635.DOI: 10.11772/j.issn.1001-9081.2022040541

• Frontier and comprehensive applications • Previous Articles Next Articles

Cross-view geo-localization method based on multi-task joint learning

Xianlan WANG¹, Jinkun ZHOU¹, Nan MU², Chen WANG³()

^1.Wuhan Research Institute of Posts and Telecommunications，Wuhan Hubei 430074，China
^2.College of Computer Science，Sichuan Normal University，Chengdu Sichuan 610101，China
^3.Nanjing Fiberhome Tiandi Communication Technology Company Limited，Nanjing Jiangsu 210019，China

Received:2022-04-18 Revised:2022-07-04 Accepted:2022-07-05 Online:2022-08-12 Published:2023-05-10
Contact: Chen WANG
About author:WANG Xianlan， born in 1969， senior engineer. Her research interests include artificial intelligence， data communication.
ZHOU Jinkun， born in 1995， M. S. candidate. His research interests include deep learning， computer vision.
MU Nan， born in 1991， Ph. D.， lecturer. His research interests include image processing， computer vision.
WANG Chen， born in 1979， M. S.， senior engineer. His research interests include network security， deep learning.
Supported by:
National Natural Science Foundation of China(62006165)

基于多任务联合学习的跨视角地理定位方法

王先兰¹, 周金坤¹, 穆楠², 王晨³()

^1.武汉邮电科学研究院, 武汉 430074
^2.四川师范大学计算机科学学院, 成都 610101
^3.南京烽火天地通信科技有限公司, 南京 210019

通讯作者: 王晨
作者简介:王先兰（1969—），女，湖北荆州人，高级工程师，主要研究方向：人工智能、数据通信
周金坤（1995—），男，湖北荆州人，硕士研究生，主要研究方向：深度学习、计算机视觉
穆楠（1991—），男，河南南阳人，讲师，博士，主要研究方向：图像处理、计算机视觉
王晨（1979—），男，江苏南京人，高级工程师，硕士，主要研究方向：网络安全、深度学习。wangchen5005@fiberhome.com
基金资助:
国家自然科学基金资助项目(62006165)

Abstract

Abstract:

Multi-task Joint Learning Model （MJLM） was proposed to solve the performance improvement bottleneck problem caused by the separation of viewpoint-invariant feature and view transformation method in the existing cross-view geo-localization methods. MJLM was made up of a proactive image generative model and a posterior image retrieval model. In the proactive generative model， firstly， Inverse Perspective Mapping （IPM） for coordinate transformation was used to explicitly bridge the spatial domain difference so that the spatial geometric features of the projected image and the real satellite image were approximately the same. Then， the proposed Cross-View Generative Adversarial Network （CVGAN） was used to match and restore the image contents and textures at a fine-grained level implicitly and synthesize smoother and more real satellite images. The posterior retrieval model was composed of Multi-view and Multi-supervision Network （MMNet）， which could perform image retrieval tasks with multi-scale features and multi-supervised learning. Experimental results on Unmanned Aerial Vehicle （UAV） dataset University-1652 show that MJLM achieves the Average Precision （AP） of 89.22% and Recall （R@1） of 87.54%， respectively. Compared with LPN （Local Pattern Network） and MSBA （MultiScale Block Attention）， MJLM has the R@1 improved by 15.29% and 1.07% respectively. It can be seen that MJLM processes the cross-view image synthesis and retrieval tasks together to realize the fusion of view transformation and viewpoint-invariant feature methods in an aggregation， improves the precision and robustness of cross-view geo-localization significantly and verifies the feasibility of the UAV localization.

Key words: cross-view geo-localization, Unmanned Aerial Vehicle (UAV) image localization, view transformation, feature extraction, deep learning

摘要：

针对现有跨视角地理定位方法中视点不变特征与视角转换方法割裂导致的性能提升瓶颈问题，提出多任务联合学习模型（MJLM）。MJLM由前置图像生成模型和后置图像检索模型组成。前置生成模型首先使用逆透视映射（IPM）进行坐标变换，显式地弥合空间域差，使投影图像与真实卫星图的空间几何特征大致相同；然后通过提出的跨视角生成对抗网络（CVGAN）隐式地对图像内容及纹理进行细粒度的匹配和修复，并合成出更平滑且真实的卫星图像。后置检索模型由多视角多监督网络（MMNet）构成，能够兼顾多尺度特征和多监督学习的图像检索任务。在University-1652（无人机定位数据集）上进行实验，结果显示MJLM对无人机（UAV）定位任务的平均精确率（AP）及召回率（R@1）分别达到89.22%和87.54%，与LPN （Local Pattern Network）和MSBA （MultiScale Block Attention）相比，MJLM在R@1上分别提升了15.29%和1.07%。可见，MJLM能在一个聚合框架体系内联合处理跨视角图像生成任务及检索任务，实现基于视角转换与视点不变特征方法的融合，有效提升跨视角地理定位的精度和鲁棒性，验证UAV定位的可行性。

关键词: 跨视角地理定位, 无人机图像定位, 视角转换, 特征提取, 深度学习

CLC Number:

TP391.4

Xianlan WANG, Jinkun ZHOU, Nan MU, Chen WANG. Cross-view geo-localization method based on multi-task joint learning[J]. Journal of Computer Applications, 2023, 43(5): 1625-1635.

王先兰, 周金坤, 穆楠, 王晨. 基于多任务联合学习的跨视角地理定位方法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1625-1635.

Figures/Tables 18

Fig. 1 Schematic diagram of UAV image localization and navigation tasks

Fig. 2 Proactive image generation model based on view transformation

Fig. 3 Schematic diagram of IPM of UAV localization scene

Fig. 4 Effect diagram of coordinate transformation based on IPM

Fig. 5 Schematic diagram of generator architecture

Fig.6 Schematic diagram of generator details

Tab. 1 Network structure parameters of generator

参数名称	输出特征尺寸
$I p s$	3， 256， 256
1×1 Conv	32， 256， 256
（enc1）残差下采样模块RD1	128， 128， 128
（enc2）残差下采样模块RD2	256， 64， 64
（enc3）残差下采样模块RD3	512， 32， 32
网络瓶颈层×6	512， 32， 32
+嵌合（enc3）残差上采样模块RU1	256， 64， 64
+嵌合（enc2）自注意力模块	512， 64， 64
残差上采样模块RU2	128 128， 128
+嵌合（enc1）残差上采样模块RU3	32， 256， 256
3×3 Conv + Tanh	3， 256， 256

Tab. 1 Network structure parameters of generator

参数名称	输出特征尺寸
$I p s$	3， 256， 256
1×1 Conv	32， 256， 256
（enc1）残差下采样模块RD1	128， 128， 128
（enc2）残差下采样模块RD2	256， 64， 64
（enc3）残差下采样模块RD3	512， 32， 32
网络瓶颈层×6	512， 32， 32
+嵌合（enc3）残差上采样模块RU1	256， 64， 64
+嵌合（enc2）自注意力模块	512， 64， 64
残差上采样模块RU2	128 128， 128
+嵌合（enc1）残差上采样模块RU3	32， 256， 256
3×3 Conv + Tanh	3， 256， 256

Tab. 2 Network structure parameters of discriminator

参数名称	输出特征尺寸
$I s$ / $G (I p s)$	3， 256， 256
4×4 Conv + LeakyReLU（0.2）	64， 128， 128
4×4 Conv + LeakyReLU（0.2）	128， 64， 64
非局部自注意力模块	128， 64， 64
4×4 Conv + LeakyReLU（0.2）	256， 32， 32
4×4 Conv + LeakyReLU（0.2）	512， 32， 32
4×4 Conv	1， 32， 32

Tab. 2 Network structure parameters of discriminator

参数名称	输出特征尺寸
$I s$ / $G (I p s)$	3， 256， 256
4×4 Conv + LeakyReLU（0.2）	64， 128， 128
4×4 Conv + LeakyReLU（0.2）	128， 64， 64
非局部自注意力模块	128， 64， 64
4×4 Conv + LeakyReLU（0.2）	256， 32， 32
4×4 Conv + LeakyReLU（0.2）	512， 32， 32
4×4 Conv	1， 32， 32

Fig. 7 Schematic diagram of MJLM architecture

Fig. 8 Samples of images from University-1652 dataset

Tab. 3 Performance comparison between MJLM and state-of-the-art methods on University-1652 dataset

方法	骨干网络	无人机→卫星		卫星→无人机
方法	骨干网络	R@1	AP	R@1	AP
ORB^［34］	—	11.31	19.36	28.46	30.12
SIFT^［35］	—	21.47	29.47	41.57	35.43
SURF^［36］	—	19.69	36.29	45.26	34.13
加权软边界三元组损失^［10］	VGG16	53.21	58.03	65.62	54.47
实例损失^［17］	ResNet-50	58.23	62.91	74.47	59.45
LCM^［20］	ResNet-50	66.65	70.82	79.89	65.38
SFPN^［19］	ResNet-50	70.83	77.36	80.26	71.58
LPN^［18］	ResNet-50	75.93	79.14	86.45	74.79
PCL^［26］	ResNet-50	83.27	87.32	91.78	82.18
MMNet^［25］	ResNet-50	83.97	86.96	90.15	84.69
FSRA^［22］	Vit-S	85.50	87.53	89.73	84.94
MSBA^［21］	ResNet-50	86.61	88.55	92.15	84.45
IPM+CVGAN+LPN	ResNet-50	81.58	85.45	—	—
MJLM	ResNet-50	87.54	89.22	—	—

Fig. 9 Result graphs of UAV localization tasks

Tab. 4 Ablation study results of proactive image generation model on University-1652 dataset

模型	RMSE（↓）	SSIM（↑）	PSNR（↑）	SD（↑）
Ips vs Is （i.）	49.154	0.459	19.546	16.421
w/o R （ii.）	39.638	0.799	30.232	31.678
w / LPN （iii.）	39.304	0.816	30.644	31.824
w / MMNet （iv.）	39.289	0.821	30.651	31.815

Tab. 5 Ablation study results of posterior image retrieval model on University-1652 dataset

模型	无人机→卫星
模型	R@1	R@5	R@10	AP
MMNet	83.97	88.84	93.29	86.96
w/o IPM（i.）	85.73.	90.63	95.10	87.85
w/o G&D（ii.）	85.42	90.85	95.77	87.33
w/o $L c G A N$ （iii.）	86.95	91.71	96.46	88.81
w/ $G (I p s)$ （iv.）	86.53	91.18	96.01	88.74
MJLM	87.54	92.33	96.95	89.22

Tab. 5 Ablation study results of posterior image retrieval model on University-1652 dataset

模型	无人机→卫星
模型	R@1	R@5	R@10	AP
MMNet	83.97	88.84	93.29	86.96
w/o IPM（i.）	85.73.	90.63	95.10	87.85
w/o G&D（ii.）	85.42	90.85	95.77	87.33
w/o $L c G A N$ （iii.）	86.95	91.71	96.46	88.81
w/ $G (I p s)$ （iv.）	86.53	91.18	96.01	88.74
MJLM	87.54	92.33	96.95	89.22

Tab. 6 Influence of shooting distance on localization performance on University-1652 dataset

距离	无人机→卫星
距离	R@1	AP
全部	87.54	89.22
短	87.75	89.59
中	88.99	91.84
长	85.97	87.87

Tab. 7 Verification results of offset-invariance on University-1652 dataset

偏移像素	无人机→卫星
偏移像素	R@1	AP
0	87.54	89.22
10	87.19	88.93
20	85.70	86.75
30	84.57	85.11
40	81.21	81.00
50	76.92	77.44

Fig. 10 Comparison map of offset-invariance ablation experiment

Tab. 8 Verification results of rotation-invariance on University-1652 dataset

旋转角度/（°）		无人机→卫星
Query集	Gallery集	R@1/%	AP/%
0	0	87.54	89.22
45	0	85.97	85.13
90	0	81.38	82.94
135	0	84.19	84.61
180	0	86.15	88.49
32	75	85.81	86.26
216	87	83.44	83.17

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Cross-view geo-localization method based on multi-task joint learning

基于多任务联合学习的跨视角地理定位方法

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