基于强化学习数据合成的典型遥感目标检测
收稿日期: 2025-03-10
修回日期: 2025-03-16
录用日期: 2025-05-15
网络出版日期: 2025-05-30
基金资助
国家自然科学基金(62125102)
Typical remote sensing target detection with data synthesis based on reinforcement learning
Received date: 2025-03-10
Revised date: 2025-03-16
Accepted date: 2025-05-15
Online published: 2025-05-30
Supported by
National Natural Science Foundation of China(62125102)
遥感目标检测，特别是对车辆、飞机等遥感目标的检测，在交通监测、军事侦察等方面具有重要的应用价值，对相关技术的进步也起到了积极的推动作用。然而，遥感目标检测仍面临诸多挑战，包括遥感图像标注成本高，以及复杂天气条件会对目标检测性能产生干扰。为解决上述问题，提出了一种融合强化学习与可控图像渲染的遥感图像合成方法，将虚实混合的遥感图像生成任务转化为基于强化学习的场景参数搜索问题。同时，引入天气干扰因素，通过模拟云层、沙尘和雾气等复杂天气条件对训练数据进行增强，提升数据在复杂环境下的适应性和真实性。实验结果表明，相较于传统方法，所提方法在多项评价指标上均取得显著提升，最高性能提升达4.2%。这验证了所提方法的有效性，显示了合成遥感图像及其复杂天气效果在提升目标检测模型精度方面的潜力，为遥感目标检测技术的发展提供了有力支持。
范天麒 , 邹征夏 , 史振威 . 基于强化学习数据合成的典型遥感目标检测[J]. 航空学报, 2025 , 46(23) : 631955 -631955 . DOI: 10.7527/S1000-6893.2025.31955
Remote sensing detection of targets such as vehicles and aircraft is essential for traffic monitoring and military reconnaissance， and it continues to drive advances in related technologies. However， remote sensing target detection still faces numerous challenges， including the high cost of image annotation and the interference of complex weather conditions on detection performance. To address these issues， a novel image synthesis method that integrates reinforcement learning with controllable image rendering is proposed， transforming the generation of mixed real and synthetic remote sensing data into a reinforcement learning-based scene parameter search problem. Additionally， weather disturbance factors are introduced by simulating clouds， dust， and fog to augment the training data， enhancing its adaptability and realism in complex environments. Experimental results show that， compared with traditional methods， the proposed method achieves significant improvements across multiple evaluation metrics， with a maximum performance gain of 4.2%. This verifies the effectiveness of the proposed method and highlights the potential of synthetic remote sensing images and complex weather effects in improving target detection model accuracy， thereby providing strong support for the advancement of remote sensing target detection technology.
| [1] | VOULODIMOS A， DOULAMIS N， DOULAMIS A， et al. Deep learning for computer vision： A brief review［J］. Computational Intelligence and Neuroscience， 2018（1）： 7068349. |
| [2] | ZOU Z X， CHEN K Y， SHI Z W， et al. Object detection in 20 years： A survey［J］. Proceedings of the IEEE， 2023， 111（3）： 257-276. |
| [3] | 杨旭. 遥感图像目标检测技术研究［D］. 郑州： 战略支援部队信息工程大学， 2023： 1. |
| YANG X. Research on object detection technology in remote sensing images［D］. Zhengzhou： PLA Strategic Support Force Information Engineering University， 2023： 1 （in Chinese）. | |
| [4] | 欧冬秀. 交通信息技术［M］. 上海： 同济大学出版社， 2007： 9-26. |
| OU D X. Transportation information technologies［M］. Shanghai： Tongji University Press， 2007： 9-26 （in Chinese）. | |
| [5] | 常嘉伟. 基于深度学习的飞机目标检测与跟踪系统的设计与实现［D］. 北京： 北京邮电大学， 2022： 1-2. |
| CHANG J W. Design and implementation of aircraft target detection and tracking system based on deep learning［D］. Beijing： Beijing University of Posts and Telecommunications， 2022： 1-2 （in Chinese）. | |
| [6] | 方浩文， 施华君. 基于深度学习的卫星图像识别分类方法［J］. 计算机系统应用， 2019， 28（10）： 27-34. |
| FANG H W， SHI H J. Satellite image recognition and classification method based on deep learning［J］. Computer Systems & Applications， 2019， 28（10）： 27-34 （in Chinese）. | |
| [7] | 薛振锋. 合成数据集的生成与优化方法及其在岩渣分析中的应用［D］. 杭州： 浙江大学， 2021： 1-2. |
| XUE Z F. Generation and optimization of synthetic dataset and its application in rock analysis［D］. Hangzhou： Zhejiang University， 2021： 1-2 （in Chinese）. | |
| [8] | 韩俊. 基于深度学习的遥感图像车辆检测［D］. 徐州： 中国矿业大学， 2023： 5-7. |
| HAN J. Deep learning-based vehicle detection for remote sensing images［D］. Xuzhou： China University of Mining and Technology， 2023： 5-7 （in Chinese）. | |
| [9] | YANG X， SUN H， SUN X， et al. Position detection and direction prediction for arbitrary-oriented ships via multitask rotation region convolutional neural network［J］. IEEE Access， 2018， 6： 50839-50849. |
| [10] | CHENG G， SI Y J， HONG H L， et al. Cross-scale feature fusion for object detection in optical remote sensing images［J］. IEEE Geoscience and Remote Sensing Letters， 2021， 18（3）： 431-435. |
| [11] | XIE L C， XUE Y L， YE J Z. UAV aerial photography target detection algorithm based on improved YOLOv5［C］∥2022 International Conference on Machine Vision， Automatic Identification and Detection. 2022： 012024. |
| [12] | CHEN T X， YE Z， TAN Z T， et al. MiM-ISTD： Mamba-in-mamba for efficient infrared small-target detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 5007613. |
| [13] | LIU B， CHEN S B， WANG J X， et al. An oriented object detector for hazy remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2024， 62： 1001711. |
| [14] | CUBUK E D， ZOPH B， MANé D， et al. AutoAugment： Learning augmentation strategies from data［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2019： 113-123. |
| [15] | ZOPH B， CUBUK E D， GHIASI G， et al. Learning data augmentation strategies for object detection［C］∥Computer Vision-ECCV 2020. Cham： Springer， 2020： 566-583. |
| [16] | KIM J M， JANG J Y， SEO S， et al. MUM： Mix image tiles and UnMix feature tiles for semi-supervised object detection［C］∥2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2022： 14492-14501. |
| [17] | ROS G， SELLART L， MATERZYNSKA J， et al. The SYNTHIA dataset： A large collection of synthetic images for semantic segmentation of urban scenes［C］∥2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2016： 3234-3243. |
| [18] | TREMBLAY J， PRAKASH A， ACUNA D， et al. Training deep networks with synthetic data： Bridging the reality gap by domain randomization［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway： IEEE Press， 2018： 10820-10828. |
| [19] | PRAKASH A， BOOCHOON S， BROPHY M， et al. Structured domain randomization： Bridging the reality gap by context-aware synthetic data［C］∥2019 International Conference on Robotics and Automation. Piscataway： IEEE Press， 2019： 7249-7255. |
| [20] | KIM J H， HWANG Y. GAN-based synthetic data augmentation for infrared small target detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022， 60： 5002512. |
| [21] | MNHI V， KAVUKCUOGLU K， SILVER D， et al. Playing Atari with deep reinforcement learning［DB/OL］. arXiv： arXiv：1312. 5602， 2013. |
| [22] | WATKINS C J C H， DAYAN P. Q-learning［J］. Machine Learning， 1992， 8： 279-292. |
| [23] | ZOU Z X， LI W Y， SHI T Y， et al. Generative adversarial training for weakly supervised cloud matting［C］∥2019 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE Press， 2019： 201-210. |
| [24] | LI W Y， ZOU Z X， SHI Z W. Deep matting for cloud detection in remote sensing images［J］. IEEE Transactions on Geoscience and Remote Sensing， 2020， 58（12）： 8490-8502. |
| [25] | LIN T Y， DOLLáR P， GIRSHICK R， et al. Feature pyramid networks for object detection［C］∥2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2017： 936-944. |
| [26] | XIA G S， BAI X， DING J， et al. DOTA： A large-scale dataset for object detection in aerial images［C］∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2018： 3974-3983. |
| [27] | CHENG G， YUAN X， YAO X W， et al. Towards large-scale small object detection： Survey and benchmarks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（11）： 13467-13488. |
| [28] | DING J， XUE N， LONG Y， et al. Learning RoI transformer for oriented object detection in aerial images［C］∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2019： 2844-2853. |
| [29] | ZHAO Y A， LV W Y， XU S L， et al. DETRs beat YOLOs on real-time object detection［C］∥2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2024： 16965-16974. |
| [30] | QIU S H， WEN G J， FAN Y X. Occluded object detection in high-resolution remote sensing images using partial configuration object model［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing， 2017， 10（5）： 1909-1925. |
/
| 〈 |
|
〉 |
CJA