{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:55:59Z","timestamp":1760241359369,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T00:00:00Z","timestamp":1518480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV) low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW), Sliding Window (SW), Reverse Residual Order (RRO), respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change.<\/jats:p>","DOI":"10.3390\/rs10020291","type":"journal-article","created":{"date-parts":[[2018,2,13]],"date-time":"2018-02-13T14:23:48Z","timestamp":1518531828000},"page":"291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images"],"prefix":"10.3390","volume":"10","author":[{"given":"Guorong","family":"Cai","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Jimei University, Xiamen 360121, China"},{"name":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China"}]},{"given":"Songzhi","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Xiamen University, Xiamen 361000, China"}]},{"given":"Chengcai","family":"Leng","sequence":"additional","affiliation":[{"name":"School of Mathematics, Northwest University, Xi\u2032an 710127, China"}]},{"given":"Yundong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jimei University, Xiamen 360121, China"},{"name":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6573-2550","authenticated-orcid":false,"given":"Feng","family":"Lu","sequence":"additional","affiliation":[{"name":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China"},{"name":"State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, K., Pan, A., Yang, Y., Zhang, S., Ong, S.H., and Tang, H. (2017). Remote Sensing Image Registration Using Multiple Image Features. Remote Sens., 9.","DOI":"10.20944\/preprints201705.0027.v2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.isprsjprs.2014.12.025","article-title":"Automatic registration of UAV-borne sequent images and LiDAR data","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, S., Tong, X., Chen, J., Liu, X., Sun, W., Xie, H., Chen, P., Jin, Y., and Ye, Z. (2016). A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8020082"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.isprsjprs.2015.08.006","article-title":"An automated method to register airborne and terrestrial laser scanning point clouds","volume":"109","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, W., Sun, K., Li, D., Bai, T., and Sui, H. (2017). A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection. Remote Sens., 9.","DOI":"10.3390\/rs9060625"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, J., Gong, J., Guo, B., and Zhang, W. (2017). A Novel Adjustment Model for Mosaicking Low-Overlap Sweeping Images. IEEE Trans. Geosci. Remote Sens., in press.","DOI":"10.1109\/TGRS.2017.2688385"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, Y., Ou, J., He, H., Zhang, X., and Mills, J. (2016). Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses. Remote Sens., 8.","DOI":"10.3390\/rs8030204"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.isprsjprs.2016.08.013","article-title":"Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images","volume":"121","author":"Xu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., and Konolige, K. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun. ACM"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Zheng, L., Yu, M., Song, M., Stefanidis, A., Ji, Z., and Yang, C. (2016). Registration of Long-Strip Terrestrial Laser Scanning Point Clouds Using RANSAC and Closed Constraint Adjustment. Remote Sens., 8.","DOI":"10.3390\/rs8040278"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gu, Y., Li, J., and Zhang, X. (2017). Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization. Sensors, 17.","DOI":"10.3390\/s17102339"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, L., Yang, F., Zhu, H., Li, D., Li, Y., and Tang, L. (2017). An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells. Remote Sens., 9.","DOI":"10.3390\/rs9050433"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/TPAMI.2011.169","article-title":"Accelerated hypothesis generation for multi-structure data via preference analysis","volume":"34","author":"Chin","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1080\/01621459.1967.10500935","article-title":"A necessary and sufficient condition that ordinary least-squares estimators be best linear unbiased","volume":"62","author":"McElroy","year":"1967","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Huber, P.J. (1981). Robust Statistics, Springer.","DOI":"10.1002\/0471725250"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1080\/01621459.1984.10477105","article-title":"Least median of squares regression","volume":"79","author":"Rousseeuw","year":"1984","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/978-1-4615-7821-5_15","article-title":"Robust regression by means of S-estimators","volume":"Volume 26","author":"Rousseeuw","year":"1984","journal-title":"Lecture Notes in Statistics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1093\/biomet\/69.1.242","article-title":"Robust regression using repeated medians","volume":"69","author":"Siegel","year":"1982","journal-title":"Biometrika"},{"key":"ref_23","unstructured":"Chum, O., and Matas, J. (2005, January 20\u201325). Matching with PROSAC-Progressive Sample Consensus. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/TPAMI.2007.70787","article-title":"Optimal randomized RANSAC","volume":"30","author":"Chum","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, H., Mirota, D., Ishii, M., and Hager, G.D. (2008, January 23\u201328). Robust motion estimation and structure recovery from endoscopic image sequences with an adaptive scale kernel consensus estimator. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587687"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TPAMI.2011.216","article-title":"Simultaneously fitting and segmenting multiple-structure data with outliers","volume":"34","author":"Wang","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.14358\/PERS.81.1.49","article-title":"A reliable feature point matching method for oblique aerial images using the spatial relationships of the point correspondences to remove outliers","volume":"81","author":"Hu","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wong, H.S., Chin, T.J., Yu, J., and Suter, D. (2011, January 6\u201313). Dynamic and hierarchical multi-structure geometric model fitting. Proceedings of the 2011 IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126350"},{"key":"ref_29","unstructured":"Wong, H.S., Chin, T.J., Yu, J., and Suter, D. (2010, January 8\u201312). Effcient multi-dtructure robust fitting with incremental top-k lists comparison. Proceedings of the 10th Asian Conference on Computer Vision, Queenstown, New Zealand."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, J., Chin, T.J., and Suter, D. (2011, January 20\u201325). A global optimization approach to robust multi-model fitting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995608"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chin, T.J., Suter, D., and Wang, H. (2010, January 13\u201318). Multi-structure model selection via kernel optimization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539931"},{"key":"ref_32","unstructured":"Pham, T., Chin, T., Yu, J., and Suter, D. (2012, January 16\u201321). The random cluster model for robust geometric fitting. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TPAMI.2013.2296310","article-title":"The random cluster model for robust geometric fitting","volume":"36","author":"Pham","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.patcog.2012.07.005","article-title":"Mode seeking over permutations for rapid geometric model fitting","volume":"46","author":"Wong","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sheikh, Y.A., Khan, E.A., and Kanade, T. (2007, January 17\u201322). Mode-seeking by Medoidshifts. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/ICCV.2007.4408978"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Raguram, R., and Frahm, J.M. (2011, January 6\u201313). RECON: Scale-Adaptive Robust Estimation via Residual Consensus. Proceedings of the International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126382"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0167-7152(97)00020-5","article-title":"A multivariate Kolmogorov-Smirnov test of goodness of fit","volume":"35","author":"Justel","year":"1997","journal-title":"Stat. Probab. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fragoso, V., Sen, P., Rodriguez, S., and Turk, M. (2013, January 1\u20138). EVSAC: Accelerating hypotheses generation by modeling matching scores with extreme value theory. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.307"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/TPAMI.2007.70768","article-title":"Balanced exploration and exploitation model search for effcient epipolar geometry estimation","volume":"30","author":"Goshen","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Nasuto, D., and Craddock, J. (2002, January 2\u20135). NAPSAC: High noise, high dimensional robust estimation-its in the bag. Proceedings of the British Machine Vision Conference, Cardiff, UK."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11263-013-0643-y","article-title":"Sampling minimal subsets with large spans for robust estimation","volume":"106","author":"Tran","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_42","unstructured":"Sattler, T., Leibe, B., and Kobbelt, L. (2, January 29). SCRAMSAC: Improving RANSAC\u2019s efficiency with a spatial consistency filter. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, K.H., and Lee, S.W. (2013, January 1\u20138). Deterministic fitting of multiple structures using iterative MaxFS with inlier scale estimation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.12"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.cviu.2016.10.003","article-title":"Efficient guided hypothesis generation for multi-structure epipolar geometry estimation","volume":"154","author":"Lai","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.isprsjprs.2012.09.012","article-title":"Estimating the fundamental matrix under pure translation and radial distortion","volume":"74","author":"Steger","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.isprsjprs.2017.05.007","article-title":"Feature matching evaluation for multimodal correspondence","volume":"129","author":"Tombari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2017.2751567","article-title":"Robust Harris Corner Matching Based on the Quasi-Homography Transform and Self-Adaptive Window for Wide-Baseline Stereo Images","volume":"56","author":"Yao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TGRS.2015.2453126","article-title":"Efficient rotation-scaling-translation parameter estimation based on the fractal image model","volume":"54","author":"Uss","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wu, T., Hu, X., and Ye, L. (2016). Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements. Remote Sens., 8.","DOI":"10.3390\/rs8050383"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, B., Jiang, W., Shan, J., Zhang, J., and Li, L. (2016). Investigation on the weighted ransac approaches for building roof plane segmentation from lidar point clouds. Remote Sens., 8.","DOI":"10.3390\/rs8010005"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7032","DOI":"10.1109\/TGRS.2017.2738439","article-title":"Topologically Aware Building Rooftop Reconstruction From Airborne Laser Scanning Point Clouds","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/TGRS.2016.2617819","article-title":"Road Curb Extraction from Mobile LiDAR Point Clouds","volume":"55","author":"Xu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4790","DOI":"10.1109\/TGRS.2016.2551546","article-title":"Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data","volume":"54","author":"Nurunnabi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2017.10.001","article-title":"Pairwise registration of TLS point clouds using convariance descriptors and a non-cooperative game","volume":"134","author":"Zai","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.1016\/j.sigpro.2013.04.008","article-title":"Perspective-SIFT: An efficient tool for low-altitude remote sensing image registration","volume":"93","author":"Cai","year":"2013","journal-title":"Signal Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:55:00Z","timestamp":1760194500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,13]]},"references-count":55,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020291"],"URL":"https:\/\/doi.org\/10.3390\/rs10020291","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,2,13]]}}}