{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:49:27Z","timestamp":1769557767789,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Research Strengthening Program of China (173 Program)","award":["2020-JCJQ-ZD-015-00-03"],"award-info":[{"award-number":["2020-JCJQ-ZD-015-00-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image matching is a key research issue in the intelligent processing of remote sensing images. Due to the large phase differences or apparent differences in ground features between unmanned aerial vehicle imagery and satellite imagery, as well as the large number of sparsely textured areas, image matching between the two types of imagery is very difficult. Tackling the difficult problem of matching unmanned aerial vehicle imagery and satellite imagery, a feature sparse region detection and matching enhancement algorithm (SD-ME) is proposed in this study. First, the SuperGlue algorithm was used to initially match the two images, and feature-sparse region detection was performed with the help of the image features and initial matching results, with the detected feature sparse areas stored in a linked list one by one. Then, according to the order of storage, feature re-extraction was performed on the feature-sparse areas individually, and an adaptive threshold feature screening algorithm was proposed to filter and screen the re-extracted features. This retains only high-confidence features in the region and improves the reliability of matching enhancement results. Finally, local features with high scores that were re-extracted in the feature-sparse areas were aggregated and input to the SuperGlue network for matching, and thus, reliable matching enhancement results were obtained. The experiment selected four pairs of un-manned aerial vehicle imagery and satellite imagery that were difficult to match and compared the SD-ME algorithm with the SIFT, ContextDesc, and SuperGlue algorithms. The results revealed that the SD-ME algorithm was far superior to other algorithms in terms of the number of correct matching points, the accuracy of matching points, and the uniformity of distribution of matching points. The number of correctly matched points in each image pair increased by an average of 95.52% compared to SuperGlue. The SD-ME algorithm can effectively improve the matching quality between unmanned aerial vehicle imagery and satellite imagery and has practical value in the fields of image registration and change detection.<\/jats:p>","DOI":"10.3390\/rs14246214","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Method for Detecting Feature-Sparse Regions and Matching Enhancement"],"prefix":"10.3390","volume":"14","author":[{"given":"Longhao","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Chaozhen","family":"Lan","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Beibei","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-1001","authenticated-orcid":false,"given":"Tian","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Zijun","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Fushan","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tu, H., Zhu, Y., and Han, C. (2022). RI-LPOH: Rotation-invariant local phase orientation histogram for multi-modal image matching. Remote Sens., 14.","DOI":"10.3390\/rs14174228"},{"key":"ref_2","first-page":"1171","article-title":"A multi-view dense matching algorithm of high resolution aerial images based on graph network","volume":"45","author":"Yan","year":"2016","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_3","first-page":"1068","article-title":"Technical framework and preliminary practices of photogrammetric remote sensing intelligent processing of multi-source satellite images","volume":"50","author":"Zhang","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cui, S., Xu, M., Ma, A., and Zhong, Y. (2020). Modality-free feature detector and descriptor for multimodal remote sensing image registration. Remote Sens., 12.","DOI":"10.3390\/rs12182937"},{"key":"ref_5","first-page":"2","article-title":"Sift-the scale invariant feature transform","volume":"2","author":"Lowe","year":"2004","journal-title":"Int. J."},{"key":"ref_6","unstructured":"Ke, Y., and Sukthankar, R. (July, January 27). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1137\/080732730","article-title":"ASIFT: A new framework for fully affine invariant image comparison","volume":"2","author":"Morel","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_8","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_9","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 2011 International conference on computer vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_10","first-page":"1823","article-title":"Deep learning based on image matching method for oblique photogrammetry","volume":"23","author":"Yang","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_11","first-page":"1727","article-title":"Heterologous images matching considering anisotropic weighted moment and absolute phase orientation","volume":"46","author":"Yao","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2020.10.019","article-title":"Universal SAR and optical image registration via a novel SIFT framework based on nonlinear diffusion and a polar spatial-frequency descriptor","volume":"171","author":"Yu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","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_14","first-page":"71","article-title":"Multimodal image registration algorithm considering grayscale and gradient information","volume":"47","author":"Yan","year":"2018","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_15","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event.","DOI":"10.1109\/CVPR.2019.00828"},{"key":"ref_16","unstructured":"Efe, U., Ince, K.G., and Alatan, A. (2019, January 19\u201325). Dfm: A performance baseline for deep feature matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Noh, H., Araujo, A., Sim, J., Weyand, T., and Han, B. (2017, January 22\u201329). Large-scale image retrieval with attentive deep local features. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.374"},{"key":"ref_18","first-page":"189","article-title":"Deep learning algorithm for feature matching of cross modality remote sensing images","volume":"50","author":"Lan","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"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 IEEE 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 16\u201318). Superglue: Learning feature matching with graph neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X. (2021, January 11\u201318). LoFTR: Detector-free local feature matching with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Montreal, QC, Canada.","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alkhatib, W., Rensing, C., and Silberbauer, J. (2017, January 27\u201329). Multi-label text classification using semantic features and dimensionality reduction with autoencoders. Proceedings of the International Conference on Language, Data and Knowledge, Nicosia, Cyprus.","DOI":"10.1007\/978-3-319-59888-8_32"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mao, W.D., Wang, M.J., Zhou, J., and Gong, M. (2019, January 3\u20138). Minglun Gong Semi-dense Stereo Matching using Dual CNNs. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa Village, HI, USA.","DOI":"10.1109\/WACV.2019.00174"},{"key":"ref_27","first-page":"2292","article-title":"Sinkhorn Distances: Lightspeed Computation of Optimal Transport","volume":"26","author":"Cuturi","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"J\u00e9gou., H., Douze., M., Schmid., C., and P\u00e9rez, P. (2010, January 13\u201318). Aggregating local descriptors into a compact image representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540039"},{"key":"ref_29","unstructured":"Qin, J.Q., Lan, C.Z., Cui, Z.X., Zhang, Y.X., and Wang, Y. (2020). A Reference Satellite Image Retrieval Method for Drone Absolute Positioning, Geomatics and Information Science of Wuhan University."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Arandjelovi\u0107, R., Gronat, P., Torii, A., Pajdla, T., and Sivic, J. (2016, January 27\u201330). NetVLAD: CNN architecture for weakly supervised place recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.572"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Luo, Z.X., Shen, T.W., Zhou, L., Zhang, J.H., Yao, Y., Li, S.W., Fang, T., and Quan, L. (2019, January 18\u201324). ContextDesc: Local descriptor augmentation with cross-modality context. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00263"},{"key":"ref_32","first-page":"9","article-title":"An Evaluation Method for the uniformity of image feature point distribution","volume":"30","author":"Zhu","year":"2010","journal-title":"Daqing Norm. Univ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:24Z","timestamp":1760146584000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":32,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246214"],"URL":"https:\/\/doi.org\/10.3390\/rs14246214","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,8]]}}}