基于不变特征的多源遥感图像舰船目标检测算法

杨曦; 张鑫; 郭浩远; 王楠楠; 高新波

doi:10.12263/DZXB.20210842

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基于不变特征的多源遥感图像舰船目标检测算法

机器学习交叉融合创新 | 更新时间：2025-12-08

- 基于不变特征的多源遥感图像舰船目标检测算法
- Invariant Features Based Ship Detection Model for Multi-source Remote Sensing Images
- 电子学报 2022年50卷第4期页码：887-899
- 作者机构：
  
  1.西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2.重庆邮电大学图像认知重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "杨曦女，1988年10月出生,陕西咸阳人.现为西安电子科技大学通信工程学院教授、博士生导师.研究方向为计算机视觉和机器学习.E-mail: yangx@xidian.edu.cn" ]
  [ "张鑫男，1998年11月出生，陕西渭南人.现为西安电子科技大学通信工程学院硕士研究生.研究方向为计算机视觉和目标检测.E-mail: xzhang_98@stu.xidian.edu.cn" ]
  [ "郭浩远男，1996年2月出生，河南郑州人.2021年获得西安电子科技大学通信工程学院硕士学位.研究方向计算机视觉和机器学习.E-mail: guohy@stu.xidian.edu.cn" ]
  [ "王楠楠男，1986年11月出生，山东安丘人.现为西安电子科技大学通信工程学院教授、博士生导师.研究方向为计算机视觉和机器学习.E-mail: nnwang@xidian.edu.cn" ]
  [ "高新波（通讯作者）男，1972年8月出生，山东莱芜人.现为西安电子科技大学电子工程学院教授、博士生导师，重庆邮电大学计算机学院教授、博士生导师.研究方向为计算机视觉和机器学习." ]
- 基金信息：
  
  国家自然科学基金(61976166;62036007;61772402;62050175;61922066;61876142);陕西省重点研发计划(2021GY-030);陕西省创新人才推进计划(2020KJXX-027);中央高校基本科研业务费(JB210115)
- DOI：10.12263/DZXB.20210842
  中图分类号： TN911.73;
- 收稿：2021-07-05，
  
  修回：2021-09-26，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
杨曦,张鑫,郭浩远等.基于不变特征的多源遥感图像舰船目标检测算法[J].电子学报,2022,50(04):887-899.

YANG Xi,ZHANG Xin,GUO Hao-yuan,et al.Invariant Features Based Ship Detection Model for Multi-source Remote Sensing Images[J].ACTA ELECTRONICA SINICA,2022,50(04):887-899.
杨曦,张鑫,郭浩远等.基于不变特征的多源遥感图像舰船目标检测算法[J].电子学报,2022,50(04):887-899. DOI： 10.12263/DZXB.20210842.

YANG Xi,ZHANG Xin,GUO Hao-yuan,et al.Invariant Features Based Ship Detection Model for Multi-source Remote Sensing Images[J].ACTA ELECTRONICA SINICA,2022,50(04):887-899. DOI： 10.12263/DZXB.20210842.

摘要

由于域偏移的存在，多源图像舰船目标检测任务面临着不同源传感器带来的图像风格差异难题.另外，为特定数据源训练特定的检测模型会消耗大量的计算资源，严重限制了其在军民用领域的工程应用.因此，设计一个通用网络以有效检测来自不同源遥感数据的舰船目标成了当下的研究热点.针对该需求，本文提出了一种基于不变特征的通用舰船目标检测方法，通过充分利用多源数据之间的共享知识实现通用遥感目标的网络检测.本方法由2部分组成：图像级的风格转换网络和特征级的域自适应网络.具体地，前者采用风格转换网络生成接近真实分布的伪多源图像，拉近多源数据之间的分布，在图像层面上学习多源数据的不变特征；为学习特征层面上多源数据的不变特征，后者通过适应网络对多源特征进行信息解耦，通过域注意力网络的自适应权重分配实现特征重组.本文在NWPU VHR-10，SSDD，HRSC和SAR-Ship-Dataset数据集上进行实验验证，结果表明：所提方法通过不变特征之间的信息互补，缓解了域偏移问题，可有效检测多源遥感数据.本文方法在上述多源数据集上的平均mAP为90.8%，相比现有主流舰船目标检测方法可以提高1.4%~10.6%.

Abstract

Due to the domain shift

ship detection in multi-source data suffers from image variations caused by different source sensors. In addition

training a specific model for a particular data source consumes high computational cost

which severely limits its practical application in military and civilian fields. Therefore

designing a universal network to effectively detect ship objects from multi-source remote sensing images has become a research hotspot. To this end

the paper proposes a universal ship detection algorithm based on invariant features

which realizes a universal remote sensing object detection network by fully utilizing the shared knowledge among multi-source data. Our method mainly consists of two parts

i.e.

an image-level style transfer network and a feature-level domain adaptive network. Specifically

the former employs style transfer network to generate pseudo-multi-source images that are close to the real distribution

narrow the distribution between multi-source data

and extract the invariant features of multi-source data at the image level; To extract invariant features at the feature level

the latter decouples the multi-source features through adaptive network

and realizes feature reorganization through adaptive weight allocation of domain attention network. We evaluate the proposed method using multiple datasets including NWPU VHR-10

SSDD

HRSC and SAR-Ship-Dataset. Experimental results show that the proposed method alleviates the problem of domain shift by complementing the information between invariant features

and can effectively detect multi-source remote sensing data. The average mAP of our method on the above-mentioned multi-source datasets is 90.8%

which exceeds 1.4%-10.6% compared with the existing mainstream ship object detection methods.

关键词

Keywords

references

种劲松 , 朱敏慧 . SAR图像舰船及其尾迹检测研究综述 [J]. 电子学报 , 2003 , 31 ( 9 ): 1356 - 1360 .

CHONG J S , ZHU M H . Survey of the study on ship and wake detection in SAR imagery [J]. Acta Electronica Sinica , 2003 , 31 ( 9 ): 1356 - 1360 . (in Chinese)

WANG C , BI F , ZHANG W , et al . An intensity-space domain CFAR method for ship detection in HR SAR images [J]. IEEE Geoscience and Remote Seneing Letters , 2017 , 14 ( 4 ): 529 - 533 .

PAPPAS O , ACHIM A , BULL D . Superpixel-level CFAR detectors for ship detection in SAR images [J]. IEEE Geoscience and Remote Sensing Letters , 2018 , 15 ( 9 ): 1397 - 1401 .

HUO W , HUANG Y , PEI J , et al . Ship detection from ocean SAR image based on local contrast variance weighted information entropy [J]. Sensors , 2018 , 18 ( 4 ): 1196 .

王明春 , 张嘉峰 , 杨子渊 , 等 . Beta分布下基于白化滤波的极化SAR图像海面舰船目标CFAR检测方法 [J]. 电子学报 , 2019 , 47 ( 9 ): 77 - 84 .

WANG M C , ZHANG J F , YANG Z Y , et , al . A CFAR detection method of ship targets in polarimetric SAR imagery based on whitening filter under Beta distributed texture [J]. Acta Electronica Sinica , 2019 , 47 ( 9 ): 77 - 84 . (in Chinese)

NIE J , ANWER R , CHOLAKKAL H , et al . Enriched feature guided refinement network for object detection [C]// Proceedings of the IEEE International Conference on Computer Vision . Kerkyra : IEEE , 2019 : 9537 - 9546 .

ZHOU X , WANG D , KRAHENBUHL P . Objects as points [EB/OL]. ( 2019-04-16 )[ 2021-07-05 ]. https://arxiv.org/abs/1904.07850v1 https://arxiv.org/abs/1904.07850v1 .

REN S , HE K , GIRSHICK R , et al . Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

PANG J , CHEN K , SHI J , et al . Libra R-CNN: Towards balanced learning for object detection [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 821 - 830 .

ZHU J Y , PARK T , ISOLA P , et al . Unpaired image-to-image translation using cycle-consistent adversarial networks [C]// Proceedings of the IEEE International Conference on Computer Vision . Venice : IEEE , 2017 : 2223 - 2232 .

GIRSHICK R . Fast R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision . Santiago : IEEE , 2015 : 1440 - 1448 .

LIN T Y , DOLLÁR P , GIRSHICK R , et al . Feature pyramid networks for object detection [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Venice : IEEE , 2017 : 2117 - 2125 .

JIANG B , LUO R , MAO J , et al . Acquisition of localization confidence for accurate object detection [C]// Proceedings of the European Conference on Computer Vision . Munich : Springer , 2018 : 784 - 799 .

LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [C]// Proceedings of the IEEE International Conference on Computer Vision . Venice : IEEE , 2017 : 2980 - 2988 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: Unified, real-time object detection [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Los Vegas : IEEE , 2016 : 779 - 788 .

REDMON J , FARHADI A . YOLO9000: Better, faster, stronger [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA : IEEE , 2017 : 7263 - 7271 .

REDMON J , FARHADI A . YOLOv3: An incremental improvement [EB/OL]. ( 2018-04-08 )[ 2021-07-05 ]. https://arxiv.org/abs/1804.02767 https://arxiv.org/abs/1804.02767 .

BOCHKOVSKIY A , WANG C Y , LIAO H Y M . YOLOv4: Optimal speed and accuracy of object detection [EB/OL]. ( 2020-04-23 )[ 2021-07-05 ]. https://arxiv.org/abs/2004.10934 https://arxiv.org/abs/2004.10934 .

LAW H , DENG J . Cornernet: Detecting objects as paired keypoints [C]// Proceedings of the European Conference on Computer Vision . Munich : Springer , 2018 : 734 - 750 .

CUI Z , LI Q , CAO Z , et al . Dense attention pyramid networks for multi-scale ship detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 11 ): 8983 - 8997 .

YANG X , LIU Q , YAN J , et al . R 3 Det: Refined singlestage detector with feature refinement for rotating object [EB/OL]. ( 2019-08-15 )[ 2021-07-05 ]. https://arxiv.org/abs/1908.05612 https://arxiv.org/abs/1908.05612 .

FU J , SUN X , WANG Z , et al . An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing , 2020 , 59 ( 2 ): 1331 - 1344 .

CHEN Y , LI W , SAKARIDIS C , et al . Domain adaptive Faster R-CNN for object detection in the wild [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 3339 - 3348 .

SAITO K , USHIKU Y , HARADA T , et al . Strong-weak distribution alignment for adaptive object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 6956 - 6965 .

WANG X , CAI Z , GAO D , et al . Towards universal object detection by domain attention [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE , 2019 : 7289 - 7298 .

GATYS L A , ECKER A S , Bethge M . Image style transfer using convolutional neural networks [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Bhubaneswar : IEEE , 2016 : 2414 - 2423 .

BERGER G , MEMISEVIC R . Incorporating long-range consistency in CNN-based texture generation [EB/OL]. ( 2016-01-03 )[ 2021-07-05 ]. https://arxiv.org/abs/1606.01286v1 https://arxiv.org/abs/1606.01286v1 .

GU S , CHEN C , LIAO J , et al . Arbitrary style transfer with deep feature reshuffle [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 8222 - 8231 .

WANG X , OXHOLM G , ZHANG D , et al . Multimodal transfer: A hierarchical deep convolutional neural network for fast artistic style transfer [C]// Proceedings of International Conference on Computer Vision and Pattern Recognition . Honolulu : IEEE , 2017 : 5239 - 5247 .

YU F , WANG D , SHELHAMER E , et al . Deep layer aggregation [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 2403 - 2412 .

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 7132 - 7141 .

CHENG G , ZHOU P , HAN J . Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 12 ): 7405 - 7415 .

LI J , QU C , SHAO J . Ship detection in SAR images based on an improved faster R-CNN [C]// SAR in Big Data Era: Models, Methods and Applications(BIGSARDATA) . Beijing : IEEE , 2017 : 1 - 6 .

LIU Z , YUAN L , WENG L , et al . A high resolution optical satellite image dataset for ship recognition and some new baselines [C]// International Conference on Pattern Recognition Applications and Methods . Porto : ICPRAM , 2017 : 324 - 331 .

WANG Y , WANG C , ZHANG H , et al . A SAR dataset of ship detection for deep learning under complex backgrounds [J]. Remote Sensing , 2019 , 11 ( 7 ): 765 .

CAI Z , VASCONCELOS N . Cascade R-CNN: Delving into high quality object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE , 2018 : 6154 - 6162 .

GUO H , YANG X , WANG N , et al . A CenterNet++ model for ship detection in SAR images [J]. Pattern Recognition , 2021 , 112 : 107787 .

MENON S , DAMIAN A , HU S , et al . PULSE: Self-supervised photo upsampling via latent space exploration of generative models [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Seattle : IEEE , 2020 : 2437 - 2445 .

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