Abstract
To tackle the problem of drone-based cross-view geo-localization, we address how to match drone-view images and satellite-view images, which is extremely challenging due to the variability of view angles and view distances. Inspired by how humans recognize aerial images, we propose an effective Attention-guided Segment Transformer (AST) structure: a novel segmentation strategy is introduced to cope with the huge variations between aerial views, and this segmentation is adaptive and non-uniform, allowing it to segment regions with corresponding relationships even after significant changes in viewpoint; furthermore, a new segment token module is designed to generate segment tokens that are concatenated with the original class token to supplement the local information. Compared to CNN-based methods, AST fully utilizes the self-attention mechanism to establish global context correlations; and the newly introduced segment token module allows AST to effectively extract local features as well—a capability not present in the vanilla vision transformer. Remarkably, AST demonstrates good robustness to viewpoint changes, even when there are overlapping regions, and this good treat is confirmed by the experimental results on the University-1652 dataset, which also show competitive performance for both tasks of drone-view target localization and drone navigation.
This work is supported by NSFC under grants U19A2071 and 61860206007, Sichuan Science and Technology Program under grant 2023YFG0334, as well as the funding from Sichuan University under grant 2020SCUNG205.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali, A., et al.: XCIT: cross-covariance image transformers. Adv. Neural Inf. Process. Syst. 34, 20014–20027 (2021)
Brown, T., et al.: Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020)
Bui, D.V., Kubo, M., Sato, H.: A part-aware attention neural network for cross-view geo-localization between UAV and satellite. J. Rob. Network. Artif. Life 9(3), 275–284 (2022)
Cao, H., et al.: Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv:2105.05537 (2021)
Chen, C.F.R., Fan, Q., Panda, R.: Crossvit: cross-attention multi-scale vision transformer for image classification. In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 357–366 (2021)
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision Pattern Recognition, vol. 1, pp. 539–546. IEEE (2005)
Chu, X., et al.: Twins: revisiting the design of spatial attention in vision transformers. Adv. Neural Inf. Process. Syst. 34, 9355–9366 (2021)
Dai, M., Hu, J., Zhuang, J., Zheng, E.: A transformer-based feature segmentation and region alignment method for UAV-view geo-localization. IEEE Trans. Circ. Syst. Video Technol. 32(7), 4376–4389 (2021)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
Ding, L., Zhou, J., Meng, L., Long, Z.: A practical cross-view image matching method between UAV and satellite for UAV-based geo-localization. Remote Sens. 13(1), 47 (2020)
Dong, X., et al.: Cswin transformer: a general vision transformer backbone with cross-shaped windows. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 12124–12134 (2022)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)
Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. Adv. Neural Inf. Process. Syst. 34, 15908–15919 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heo, B., Yun, S., Han, D., Chun, S., Choe, J., Oh, S.J.: Rethinking spatial dimensions of vision transformers. In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 11936–11945 (2021)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Hu, S., Feng, M., Nguyen, R.M., Lee, G.H.: Cvm-net: cross-view matching network for image-based ground-to-aerial geo-localization. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 7258–7267 (2018)
Kirillov, A., et al.: Segment anything. arXiv:2304.02643 (2023)
Lin, J., et al.: Joint representation learning and keypoint detection for cross-view geo-localization. IEEE Trans. Image Process. 31, 3780–3792 (2022)
Lin, T.Y., Cui, Y., Belongie, S., Hays, J.: Learning deep representations for ground-to-aerial geolocalization. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 5007–5015 (2015)
Liu, L., Li, H.: Lending orientation to neural networks for cross-view geo-localization. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 5624–5633 (2019)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 10012–10022 (2021)
Lu, Z., Pu, T., Chen, T., Lin, L.: Content-aware hierarchical representation selection for cross-view geo-localization. In: Proceedings of Asian Conference on Computer Vision, pp. 4211–4224 (2022)
Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 29 (2016)
Pan, Z., Zhuang, B., Liu, J., He, H., Cai, J.: Scalable vision transformers with hierarchical pooling. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 377–386 (2021)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)
Radenović, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655–1668 (2018)
Shi, Y., Liu, L., Yu, X., Li, H.: Spatial-aware feature aggregation for image based cross-view geo-localization. Adv. Neural Inf. Process. Syst. 32 (2019)
Tian, X., Shao, J., Ouyang, D., Shen, H.T.: UAV-satellite view synthesis for cross-view geo-localization. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4804–4815 (2021)
Toker, A., Zhou, Q., Maximov, M., Leal-Taixé, L.: Coming down to earth: satellite-to-street view synthesis for geo-localization. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 6488–6497 (2021)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wang, P., et al.: KVT: k-nn attention for boosting vision transformers. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, vol. 13684, pp. 285–302. Springer, Heidleberg (2022). https://doi.org/10.1007/978-3-031-20053-3_17
Wang, T., et al.: Each part matters: local patterns facilitate cross-view geo-localization. IEEE Trans. Circuits Syst. Video Technol. 32(2), 867–879 (2021)
Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 568–578 (2021)
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., Li, H.: Uformer: a general u-shaped transformer for image restoration. In: Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 17683–17693 (2022)
Yang, H., Lu, X., Zhu, Y.: Cross-view geo-localization with layer-to-layer transformer. Adv. Neural Inf. Process. Syst. 34, 29009–29020 (2021)
Zhai, M., Bessinger, Z., Workman, S., Jacobs, N.: Predicting ground-level scene layout from aerial imagery. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition, pp. 867–875 (2017)
Zheng, Z., Wei, Y., Yang, Y.: University-1652: a multi-view multi-source benchmark for drone-based geo-localization. In: Proceedings of 28th ACM International Conference on Multimedia, pp. 1395–1403 (2020)
Zhou, D., et al.: Deepvit: towards deeper vision transformer. arXiv:2103.11886 (2021)
Zhu, S., Shah, M., Chen, C.: Transgeo: transformer is all you need for cross-view image geo-localization. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 1162–1171 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, Z., Tang, T., Chen, J., Shi, X., Liu, Y. (2024). AST: An Attention-Guided Segment Transformer for Drone-Based Cross-View Geo-Localization. In: Zhang, FL., Sharf, A. (eds) Computational Visual Media. CVM 2024. Lecture Notes in Computer Science, vol 14593. Springer, Singapore. https://doi.org/10.1007/978-981-97-2092-7_17
Download citation
DOI: https://doi.org/10.1007/978-981-97-2092-7_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2091-0
Online ISBN: 978-981-97-2092-7
eBook Packages: Computer ScienceComputer Science (R0)