{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:00:03Z","timestamp":1760230803427,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T00:00:00Z","timestamp":1660003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PolyU Start-up Fund","award":["P0034164","P0036092"],"award-info":[{"award-number":["P0034164","P0036092"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.<\/jats:p>","DOI":"10.3390\/rs14163857","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"3857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-4560","authenticated-orcid":false,"given":"Haochen","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6900-0901","authenticated-orcid":false,"given":"Boyang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7403-9133","authenticated-orcid":false,"given":"Wenyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1181-8786","authenticated-orcid":false,"given":"Chih-Yung","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","unstructured":"Wang, L., Neumann, U., and You, S. (October, January 27). Wide-baseline image matching using line signatures. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.neucom.2015.07.137","article-title":"Hierarchical line matching based on line\u2013junction\u2013line structure descriptor and local homography estimation","volume":"184","author":"Li","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.jvcir.2013.05.006","article-title":"An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency","volume":"24","author":"Zhang","year":"2013","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1109\/TRO.2015.2489498","article-title":"Building a 3-D line-based map using stereo SLAM","volume":"31","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TRO.2019.2899783","article-title":"PL-SLAM: A stereo SLAM system through the combination of points and line segments","volume":"35","author":"Moreno","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chan, S.H., Wu, P.T., and Fu, L.C. (2018, January 7\u201310). Robust 2D indoor localization through laser SLAM and visual SLAM fusion. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00221"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chang, L., Niu, X., Liu, T., Tang, J., and Qian, C. (2019). GNSS\/INS\/LiDAR-SLAM integrated navigation system based on graph optimization. Remote Sens., 11.","DOI":"10.3390\/rs11091009"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107079","DOI":"10.1016\/j.compag.2022.107079","article-title":"Rachis detection and three-dimensional localization of cut off point for vision-based banana robot","volume":"198","author":"Wu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, H., Lin, Y., Xu, X., Chen, Z., Wu, Z., and Tang, Y. (2022). A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking. Agronomy, 12.","DOI":"10.3390\/agronomy12071520"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Khattak, S., Papachristos, C., and Alexis, K. (2019, January 2\u20139). Visual-thermal landmarks and inertial fusion for navigation in degraded visual environments. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2019.8741787"},{"key":"ref_11","unstructured":"Chen, L., Sun, L., Yang, T., Fan, L., Huang, K., and Xuanyuan, Z. (June, January 29). Rgb-t slam: A flexible slam framework by combining appearance and thermal information. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_12","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":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1109\/LGRS.2017.2738632","article-title":"A local feature descriptor based on log-Gabor filters for keypoint matching in multispectral images","volume":"14","author":"Nunes","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, S., Lv, X., Ren, J., and Li, J. (2022). A Robust 3D Density Descriptor Based on Histogram of Oriented Primary Edge Structure for SAR and Optical Image Co-Registration. Remote Sens., 14.","DOI":"10.3390\/rs14030630"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.patcog.2008.08.035","article-title":"MSLD: A robust descriptor for line matching","volume":"42","author":"Wang","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Verhagen, B., Timofte, R., and Van Gool, L. (2014, January 24\u201326). Scale-invariant line descriptors for wide baseline matching. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836061"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.isprsjprs.2017.01.006","article-title":"Line segment matching and reconstruction via exploiting coplanar cues","volume":"125","author":"Li","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2899","DOI":"10.1109\/TCSVT.2018.2873807","article-title":"Reliable line segment matching for multispectral images guided by intersection matches","volume":"29","author":"Li","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2021.09.002","article-title":"Hierarchical line segment matching for wide-baseline images via exploiting viewpoint robust local structure and geometric constraints","volume":"181","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Gool, L.V. (2006, January 7\u201313). Surf: Speeded up robust features. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_32"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201323). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.patcog.2011.08.004","article-title":"Robust line matching through line\u2013point invariants","volume":"45","author":"Fan","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_25","first-page":"4199","article-title":"Line matching in wide-baseline stereo: A top-down approach","volume":"23","author":"Yilmaz","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jia, Q., Gao, X., Fan, X., Luo, Z., Li, H., and Chen, Z. (2016, January 11\u201314). Novel coplanar line-points invariants for robust line matching across views. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_36"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2020.09.021","article-title":"Robust line feature matching based on pair-wise geometric constraints and matching redundancy","volume":"172","author":"Wang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lange, M., Schweinfurth, F., and Schilling, A. (2019, January 3\u20138). Dld: A deep learning based line descriptor for line feature matching. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968062"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Luo, Y., Qin, F., He, Y., and Liu, X. (2021, January 11\u201317). ELSD: Efficient Line Segment Detector and Descriptor. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00296"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shen, X., Xu, L., Zhang, Q., and Jia, J. (2014, January 6\u201312). Multi-modal and multi-spectral registration for natural images. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_21"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Brown, M., and S\u00fcsstrunk, S. (2011, January 20\u201325). Multi-spectral SIFT for scene category recognition. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995637"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12661","DOI":"10.3390\/s120912661","article-title":"Multispectral image feature points","volume":"12","author":"Aguilera","year":"2012","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Aguilera, C.A., Sappa, A.D., and Toledo, R. (2015, January 27\u201330). LGHD: A feature descriptor for matching across non-linear intensity variations. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350783"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ma, T., Ma, J., and Yu, K. (2019). A local feature descriptor based on oriented structure maps with guided filtering for multispectral remote sensing image matching. Remote Sens., 11.","DOI":"10.3390\/rs11080951"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.neucom.2015.11.025","article-title":"Multimodal image matching based on multimodality robust line segment descriptor","volume":"177","author":"Zhao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_36","first-page":"1","article-title":"Image features from phase congruency","volume":"1","author":"Kovesi","year":"1999","journal-title":"Videre J. Comput. Vis. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1109\/TGRS.2017.2656380","article-title":"Robust registration of multimodal remote sensing images based on structural similarity","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, X., Ai, Y., Zhang, J., and Wang, Z. (2018). A novel affine and contrast invariant descriptor for infrared and visible image registration. Remote Sens., 10.","DOI":"10.3390\/rs10040658"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.10.036","article-title":"Robust visible-infrared image matching by exploiting dominant edge orientations","volume":"127","author":"Chen","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_40","unstructured":"Aguilera, C.A., Aguilera, F.J., Sappa, A.D., Aguilera, C., and Toledo, R. (July, January 26). Learning cross-spectral similarity measures with deep convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Aguilera, C.A., Sappa, A.D., Aguilera, C., and Toledo, R. (2017). Cross-spectral local descriptors via quadruplet network. Sensors, 17.","DOI":"10.20944\/preprints201703.0061.v1"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TCYB.2016.2548484","article-title":"Multimodal image registration with line segments by selective search","volume":"47","author":"Li","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.infrared.2018.12.004","article-title":"Combining and matching keypoints and lines on multispectral images","volume":"96","author":"Fan","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, S., and Zhang, P. (2022). A New Line Matching Approach for High-Resolution Line Array Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14143287"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1016\/j.patrec.2011.06.001","article-title":"EDLines: A real-time line segment detector with a false detection control","volume":"32","author":"Akinlar","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.patrec.2012.08.009","article-title":"Multispectral piecewise planar stereo using Manhattan-world assumption","volume":"34","author":"Barrera","year":"2013","journal-title":"Pattern Recognit. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:06:19Z","timestamp":1760141179000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/3857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,9]]},"references-count":47,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14163857"],"URL":"https:\/\/doi.org\/10.3390\/rs14163857","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,9]]}}}