ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Aircraft infrared/satellite heterogenous fast matching localization method based on image translation
Received date: 2025-03-11
Revised date: 2025-03-26
Accepted date: 2025-05-22
Online published: 2025-06-06
Supported by
Key Research and Development Program of Shaanxi Province(2024CY2-GJHX-42)
In Global Navigation Satellite System-denied environments, aircraft visual navigation faces dual challenges posed by significant nighttime heterogenous image discrepancies and feature matching failures caused by large field-of-view rotations. To address these issues, this paper proposes an infrared/satellite cross-domain fast matching localization method for aircraft based on image translation, aiming to enhance nighttime matching and localization performance through image translation and rotational matching strategies. First, we construct HC_CycleGAN, a cross-domain translation model that leverages the integration of Huber loss and spatial attention mechanisms to align satellite and infrared domains. Second, we develop FastPoint, a rapid feature extraction network that integrates a deep variable convolutional layer with residual learning mechanisms to establish an R-DVM computational unit, enhancing both computational efficiency and training stability. Finally, a rotational matching method based on the L-LightGlue dynamic adaptive matching algorithm is proposed. This method combines a geometry-invariant pre-rotation matching strategy for rotational matching correction to determine the aircraft’s position in satellite image based on the inter-image transformation relationships, thereby accomplishing visual localization. Experimental results demonstrate that, compared to existing matching and localization methods, the proposed approach not only improves matching efficiency but also significantly reduces heterogenous image matching errors under rotational conditions.
Bin TANG , Xiaogang YANG , Ruitao LU , Zhenyu ZHANG , Shuang SU . Aircraft infrared/satellite heterogenous fast matching localization method based on image translation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(23) : 631961 -631961 . DOI: 10.7527/S1000-6893.2025.31961
| [1] | 杨小冈, 陈世伟, 席建祥. 飞行器异源景像匹配制导技术[M]. 北京: 科学出版社, 2016:1-20. |
| YANG X G, CHEN S W, XI J X. Matching guidance technology of aircraft heterogeneous scenes[M]. Beijing: Science Press, 2016 (in Chinese): 1-20. | |
| [2] | LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. |
| [3] | LEUTENEGGER S, CHLI M, SIEGWART R Y. BRISK: Binary robust invariant scalable keypoints[C]∥ 2011 International Conference on Computer Vision. Piscataway: IEEE Press, 2011: 2548-2555. |
| [4] | RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]∥ 2011 International Conference on Computer Vision. Piscataway: IEEE Press, 2011: 2564-2571. |
| [5] | 冯旭刚, 阮善会, 王正兵, 等. 基于局部和全局特征的电力设备红外和可见光图像匹配方法[J]. 电工技术学报, 2025, 40(7): 2236-2246, 2305. |
| FENG X G, RUAN S H, WANG Z B, et al. Infrared and visible image matching method for power equipment based on local and global features[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2236-2246, 2305 (in Chinese). | |
| [6] | ONO Y, TRULLS E, FUA P, et al. LF-Net: Learning local features from images[C]∥Advances in Neural Informa-tion Processing Systems. Red Hook: Curran Associates, 2018: 1-11. |
| [7] | REVAUD J, WEINZAEPFEL P, DE SOUZA C, et al. R2D2: Repeatable and Reliable Detector and Descript-or[C]∥33rd Conference on Neural Information Process-ing Systems (NeurIPS). Red Hook: Curran Associates, 2019: 1-11. |
| [8] | SINGH PARIHAR U, GUJARATHI A, MEHTA K, et al. RoRD: Rotation-robust descriptors and orthographic views for local feature matching[C]∥ 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2021: 1593-1600. |
| [9] | LUO Z X, ZHOU L, BAI X Y, et al. ASLFeat: Learning local features of accurate shape and localization[C]∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 6588-6597. |
| [10] | FAN B, ZHOU J J, FENG W S, et al. Learning semantic-aware local features for long term visual localization[J]. IEEE Transactions on Image Processing, 2022, 31: 4842-4855. |
| [11] | SHEN X L, WANG C, LI X, et al. RF-net: An end-to-end image matching network based on receptive field[C]∥ 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2019: 8124-8132. |
| [12] | BHOWMIK A, GUMHOLD S, ROTHER C, et al. Reinforced feature points: Optimizing feature detection and description for a high-level task[C]∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 4947-4956. |
| [13] | BARROSO-LAGUNA A, VERDIE Y, BUSAM B, et al. HDD-net: Hybrid detector descriptor with mutual interactive learning[C]∥Computer Vision-ACCV 2020. Berlin: Springer, 2021: 500-516. |
| [14] | ZHANG Y Y, WANG J G, XU S B, et al. MLIFeat: Multi-level information fusion based deep local features[C]∥Computer Vision-ACCV 2020. Berlin: Springer, 2021: 403-419. |
| [15] | SUWANWIMOLKUL S, KOMORITA S, TASAKA K. Learning of low-level feature keypoints for accurate and robust detection[C]∥ 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). Piscataway: IEEE Press, 2021: 2261-2270. |
| [16] | WANG X J, LIU Z Y, HU Y, et al. FeatureBooster: Boosting feature descriptors with a lightweight neural network[C]∥ 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2023: 7630-7639. |
| [17] | XUE F, BUDVYTIS I, CIPOLLA R. SFD2: Semantic-guided feature detection and description[C]∥ 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2023: 5206-5216. |
| [18] | DETONE D, MALISIEWICZ T, RABINOVICH A. SuperPoint: Self-supervised interest point detection and description[C]∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway: IEEE Press, 2018: 337-33712. |
| [19] | SARLIN P E, DETONE D, MALISIEWICZ T, et al. SuperGlue: Learning feature matching with graph neural networks[C]∥ 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 4937-4946. |
| [20] | EFE U, INCE K G, AYDIN ALATAN A. DFM: A performance baseline for deep feature matching[C]∥ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway: IEEE Press, 2021: 4279-4288. |
| [21] | ZHOU Q J, SATTLER T, LEAL-TAIXé L. Patch2Pix: Epipolar-guided pixel-level correspondences[C]∥ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 4667-4676. |
| [22] | HUANG D H, CHEN Y, LIU Y, et al. Adaptive assignment for geometry aware local feature matching[C]∥ 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2023: 5425-5434. |
| [23] | DUSMANU M, ROCCO I, PAJDLA T, et al. D2-net: A trainable CNN for joint description and detection of local features[C]∥ 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2019: 8084-8093. |
| [24] | JIANG W, TRULLS E, HOSANG J, et al. COTR: Correspondence transformer for matching across images[C]∥ 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2021: 6187-6197. |
| [25] | TUZCUO?LU ?, K?KSAL A, SOFU B, et al. XoFTR: Cross-modal feature matching transformer[C]∥ 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway: IEEE Press, 2024: 4275-4286. |
| [26] | 叶熠彬, 滕锡超, 于起峰, 等. 基于MatchNet和多点匹配约束的可见光-SAR图像匹配[J]. 航空学报, 2024, 45(10): 329162. |
| YE Y B, TENG X C, YU Q F, et al. Optical-SAR image matching based on MatchNet and multi-point matching constraint[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(10): 329162 (in Chinese). | |
| [27] | GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks[C]∥ 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 2414-2423. |
| [28] | ZHAO J W, YANG D F, LI Y F, et al. Intelligent matching method for heterogeneous remote sensing images based on style transfer[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 6723-6731. |
| [29] | ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]∥ 2017 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2017: 2242-2251. |
| [30] | 李清格, 杨小冈, 卢瑞涛, 等. 基于GCI-CycleGAN风格迁移的跨模态地理定位方法[J]. 红外与激光工程, 2023, 52(7): 319-331. |
| LI Q G, YANG X G, LU R T, et al. Cross-modal geo-localization method based on GCI-CycleGAN style translation[J]. Infrared and Laser Engineering, 2023, 52(7): 319-331 (in Chinese). | |
| [31] | 李清格, 杨小冈, 卢瑞涛, 等. 基于红外影像层次旋转匹配的飞行器定位方法[J]. 红外与激光工程, 2024, 53(5): 20240086. |
| LI Q G, YANG X G, LU R T, et al. Aircraft localization method based on hierarchical rotation matching of infrared images[J]. Infrared and Laser Engineering, 2024, 53(5): 20240086 (in Chinese). |
/
| 〈 |
|
〉 |