{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:31:10Z","timestamp":1775021470062,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Nature Science Foundation of Shaanxi","award":["2022JQ-653"],"award-info":[{"award-number":["2022JQ-653"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Template matching is the fundamental task in remote sensing image processing of air- and space-based platforms. Due to the heterogeneous image sources, different scales and different viewpoints, the realization of a general end-to-end matching model is still a challenging task. Considering the abovementioned problems, we propose a cross-view remote sensing image matching method. Firstly, a spatial attention map was proposed to solve the problem of the domain gap. It is produced by two-dimensional Gaussian distribution and eliminates the distance between the distributed heterogeneous features. Secondly, in order to perform matching at different flight altitudes, a multi-scale matching method was proposed to perform matching on three down-sampling scales in turn and confirm the optimal result. Thirdly, to improve the adaptability of the viewpoint changes, a pixel-wise consensus method based on a correlation layer was applied. Finally, we trained the proposed model based on weakly supervised learning, which does not require extensive annotation but only labels one pair of feature points of the template image and search image. The robustness and effectiveness of the proposed methods were demonstrated by evaluation on various datasets. Our method accommodates three types of template matching with different viewpoints, including SAR to RGB, infrared to RGB, and RGB to RGB.<\/jats:p>","DOI":"10.3390\/rs15092426","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T08:12:11Z","timestamp":1683274331000},"page":"2426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Cross-Viewpoint Template Matching Based on Heterogeneous Feature Alignment and Pixel-Wise Consensus for Air- and Space-Based Platforms"],"prefix":"10.3390","volume":"15","author":[{"given":"Tian","family":"Hui","sequence":"first","affiliation":[{"name":"Institute of Unmanned System Research, Northwestern Polytechnical University, Xi\u2019an 710000, China"}]},{"given":"Yuelei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System Research, Northwestern Polytechnical University, Xi\u2019an 710000, China"}]},{"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System Research, Northwestern Polytechnical University, Xi\u2019an 710000, China"}]},{"given":"Chaofeng","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System Research, Northwestern Polytechnical University, Xi\u2019an 710000, China"}]},{"given":"Jarhinbek","family":"Rasol","sequence":"additional","affiliation":[{"name":"Institute of Unmanned System Research, Northwestern Polytechnical University, Xi\u2019an 710000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sattler, T., Maddern, W., Toft, C., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., and Sivic, J. (2018, January 18\u201323). Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00897"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/TPAMI.2019.2952114","article-title":"InLoc: Indoor Visual Localization with Dense Matching and View Synthesis","volume":"43","author":"Taira","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ham, B., Cho, M., Schmid, C., and Ponce, J. (2016, January 27\u201330). Proposal Flow. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.378"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.patcog.2016.05.028","article-title":"Discriminant deep belief network for high-resolution SAR image classification","volume":"61","author":"Zhao","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","article-title":"SAR Image segmentation based on convolutional-wavelet neural network and markov random field","volume":"64","author":"Alali","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1109\/TGRS.2017.2689018","article-title":"Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification","volume":"55","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Altwaijry, H., Trulls, E., Hays, J., Fua, P., and Belongie, S. (2016, January 27\u201330). Learning to Match Aerial Images with Deep Attentive Architectures. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.385"},{"key":"ref_10","unstructured":"Han, X., Leung, T., Jia, Y., Sukthankar, R., and Berg, A.C. (2015, January 7\u201312). Matchnet: Unifying feature and metric learning for patch-based matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Rocco, I., Pajdla, T., Pollefeys, M., Sivic, J., Torii, A., and Sattler, T. (2019, January 15\u201320). D2-Net: A Trainable CNN for Joint Description and Detection of Local Features. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_12","first-page":"1","article-title":"Cross-Modality Image Matching Network with Modality-Invariant Feature Representation for Airborne-Ground Thermal Infrared and Visible Datasets","volume":"60","author":"Cui","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1109\/TNNLS.2018.2888757","article-title":"A Novel Neural Network for Remote Sensing Image Matching","volume":"30","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1109\/JSTARS.2019.2916560","article-title":"Registration of Multi-modal Remote Sensing Image Based on Deep Fully Convolutional Neural Network","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, H., and Huang, Y. (2021). A Rotation-Invariant Optical and SAR Image Registration Algorithm Based on Deep and Gaussian Features. Remote Sens., 13.","DOI":"10.3390\/rs13132628"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_17","unstructured":"Bay, H., and Tuytelaars, T. (2006). Computer Vision\u2014ECCV 2006, Proceedings of the 9th European Conference on Computer Vision, Graz, Austria, 7\u201313 May 2006, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovic, R., and Sivic, J. (2017, January 21\u201326). Convolutional Neural Network Architecture for Geometric Matching. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.12"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). SuperPoint: Self-Supervised Interest Point Detection and Description. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., DeTone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 13\u201319). SuperGlue: Learning Feature Matching with Graph Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/TPAMI.2020.3016711","article-title":"NCNet: Neighbourhood Consensus Networks for Estimating Image Correspondences","volume":"44","author":"Rocco","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Sattler, T., and Leal-Taix\u00e9, L. (2021, January 20\u201325). Patch2Pix: Epipolar-Guided Pixel-Level Correspondences. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00464"},{"key":"ref_23","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H.S. (2016). Computer Vision\u2014ECCV 2016 Workshops. ECCV 2016, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201323). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 15\u201320). SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2021, October 01). Very Deep Convolutional Networks for Large-Scale Image Recognition. Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_27","unstructured":"Bochkovskiy, A., Wang, C., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_28","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., and Brox, T. (2015, January 7\u201313). FlowNet: Learning Optical Flow with Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.316"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., and Zhu, X. (2018). The SEN1-2 Dataset for Deep Learning in SAR-RGB Data Fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-141-2018"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wei, Y., and Yang, Y. (2020). University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization. arXiv.","DOI":"10.1145\/3394171.3413896"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/TIP.2019.2959244","article-title":"RIFT: Multi-Modal Image Matching Based on Radiation-Variation Insensitive Feature Transform","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2021.09.012","article-title":"A deep learning semantic template matching framework for remote sensing image registration","volume":"181","author":"Li","year":"2021","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-Like Algorithm for SAR Images","volume":"53","author":"Dellinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:29:43Z","timestamp":1760124583000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092426"],"URL":"https:\/\/doi.org\/10.3390\/rs15092426","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}