{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T04:52:53Z","timestamp":1769143973217,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61903279"],"award-info":[{"award-number":["61903279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art.<\/jats:p>","DOI":"10.3390\/rs14164038","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"4038","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment"],"prefix":"10.3390","volume":"14","author":[{"given":"Yujie","family":"Zhang","sequence":"first","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Xiaoguang","family":"Mei","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Yong","family":"Ma","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Xingyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"given":"Zongyi","family":"Peng","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5893-4090","authenticated-orcid":false,"given":"Jun","family":"Huang","sequence":"additional","affiliation":[{"name":"Electronic Information School, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Side-Scan Sonar Analysis Using ROI Analysis and Deep Neural Networks","volume":"60","author":"Wawrzyniak","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"1","article-title":"Variational Pansharpening by Exploiting Cartoon-Texture Similarities","volume":"60","author":"Tian","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Luo, X., Lai, G., Wang, X., Jin, Y., He, X., Xu, W., and Hou, W. (2021). UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features. Remote Sens., 13.","DOI":"10.3390\/rs13183605"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, Z., Peng, C., Wang, Y., and Gong, W. (2022). UAV Image Stitching Based on Optimal Seam and Half-Projective Warp. Remote Sens., 14.","DOI":"10.3390\/rs14051068"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4465","DOI":"10.1109\/JSTARS.2021.3061505","article-title":"UAV Image Stitching Based on Mesh-Guided Deformation and Ground Constraint","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MGRS.2020.2979764","article-title":"Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox","volume":"8","author":"Rasti","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xue, W., Zhang, Z., and Chen, S. (2021). Ghost Elimination via Multi-Component Collaboration for Unmanned Aerial Vehicle Remote Sensing Image Stitching. Remote Sens., 13.","DOI":"10.3390\/rs13071388"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Leonardis, A., Bischof, H., and Pinz, A. (2006). SURF: Speeded Up Robust Features. Proceedings of the Computer Vision\u2014ECCV 2006, Springer.","DOI":"10.1007\/11744023"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gong, X., Yao, F., Ma, J., Jiang, J., Lu, T., Zhang, Y., and Zhou, H. (2022). Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency. Remote Sens., 14.","DOI":"10.3390\/rs14112606"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_11","first-page":"1","article-title":"Hyperspectral Image Stitching via Optimal Seamline Detection","volume":"19","author":"Peng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6184","DOI":"10.1109\/TIP.2021.3092828","article-title":"Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images","volume":"30","author":"Nie","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/TIP.2017.2749145","article-title":"Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images","volume":"27","author":"Zhou","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","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_15","unstructured":"Tian, Y., Barroso-Laguna, A., Ng, T., Balntas, V., and Mikolajczyk, K. (2020). HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Luo, Z., Zhou, L., Bai, X., Chen, H., Zhang, J., Yao, Y., Li, S., Fang, T., and Quan, L. (2020, January 13\u201319). ASLFeat: Learning Local Features of Accurate Shape and Localization. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00662"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image Matching from Handcrafted to Deep Features: A Survey","volume":"129","author":"Ma","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","first-page":"1","article-title":"Local Affine Preservation With Motion Consistency for Feature Matching of Remote Sensing Images","volume":"60","author":"Ye","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/TGRS.2015.2441954","article-title":"Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming","volume":"53","author":"Ma","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","article-title":"Locality Preserving Matching","volume":"127","author":"Ma","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1109\/TGRS.2018.2820040","article-title":"Guided Locality Preserving Feature Matching for Remote Sensing Image Registration","volume":"56","author":"Ma","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6462","DOI":"10.1109\/TGRS.2019.2906183","article-title":"Multiscale Locality and Rank Preservation for Robust Feature Matching of Remote Sensing Images","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s11263-006-0002-3","article-title":"Automatic Panoramic Image Stitching using Invariant Features","volume":"74","author":"Brown","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, C.C., Pankanti, S.U., Ramamurthy, K.N., and Aravkin, A.Y. (, January 7\u201312). Adaptive as-natural-as-possible image stitching. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298719"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Natural Image Stitching with the Global Similarity Prior. Proceedings of the Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TMM.2017.2777461","article-title":"Parallax-Tolerant Image Stitching Based on Robust Elastic Warping","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yahyanejad, S., Wischounig-Strucl, D., Quaritsch, M., and Rinner, B. (September, January 29). Incremental Mosaicking of Images from Autonomous, Small-Scale UAVs. Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, MA, USA.","DOI":"10.1109\/AVSS.2010.14"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xing, C., Wang, J., and Xu, Y. (2010, January 25\u201326). A Robust Method for Mosaicking Sequence Images Obtained from UAV. Proceedings of the 2010 2nd International Conference on Information Engineering and Computer Science, Wuhan, China.","DOI":"10.1109\/ICIECS.2010.5678358"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xia, M., Yao, M., Li, L., and Lu, X. (2015, January 27\u201330). Globally consistent alignment for mosaicking aerial images. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351361"},{"key":"ref_31","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 (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3157870","article-title":"Robust Feature Matching for Remote Sensing Image Registration via Guided Hyperplane Fitting","volume":"60","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1109\/JSTARS.2020.3001022","article-title":"Automatic Stitching for Hyperspectral Images Using Robust Feature Matching and Elastic Warp","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xia, M., Yao, J., Xie, R., Lu, X., and Li, L. (2016, January 4). Robust alignment for UAV images based on adaptive adjustment. Proceedings of the 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), Cancun, Mexico.","DOI":"10.1109\/PRRS.2016.7867017"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Daniilidis, K., Maragos, P., and Paragios, N. (2010). Sparse Non-linear Least Squares Optimization for Geometric Vision. Proceedings of the Computer Vision\u2014ECCV 2010, Springer.","DOI":"10.1007\/978-3-642-15561-1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C. (2012). KAZE Features. Proceedings of the Computer Vision\u2014ECCV 2012, Springer.","DOI":"10.1007\/978-3-642-33709-3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1109\/TIP.2014.2307478","article-title":"Robust Point Matching via Vector Field Consensus","volume":"23","author":"Ma","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4038\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:02Z","timestamp":1760141522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,18]]},"references-count":39,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164038"],"URL":"https:\/\/doi.org\/10.3390\/rs14164038","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,18]]}}}