{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:37:00Z","timestamp":1763105820943,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T00:00:00Z","timestamp":1627516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Research Funds for the Universities in Heilongjiang Province","award":["2018-KYYWF-1681"],"award-info":[{"award-number":["2018-KYYWF-1681"]}]},{"name":"the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province","award":["UNPYSCT-2017086"],"award-info":[{"award-number":["UNPYSCT-2017086"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671190, 61571168"],"award-info":[{"award-number":["61671190, 61571168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003473","name":"Harbin University of Science and Technology","doi-asserted-by":"publisher","award":["LGYC2018JQ014"],"award-info":[{"award-number":["LGYC2018JQ014"]}],"id":[{"id":"10.13039\/501100003473","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and \u221211.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.<\/jats:p>","DOI":"10.3390\/sym13081380","type":"journal-article","created":{"date-parts":[[2021,7,29]],"date-time":"2021-07-29T21:21:21Z","timestamp":1627593681000},"page":"1380","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Research on Remote Sensing Image Matching with Special Texture Background"],"prefix":"10.3390","volume":"13","author":[{"given":"Sen","family":"Wang","sequence":"first","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-0375-0563","authenticated-orcid":false,"given":"Xiaoming","family":"Sun","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Pengfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Kaige","family":"Xu","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Weifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]},{"given":"Chenxu","family":"Wu","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Obanawa, H., and Shibata, H. (2020). Applications of UAV remote sensing to topographic and vegetation surveys. Unmanned Aer. Veh. Appl. Agric. Environ., 131\u2013142.","DOI":"10.1007\/978-3-030-27157-2_10"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications\u2014A review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wan, Y., Hu, X., Zhong, Y., Ma, A., Wei, L., and Zhang, L. (August, January 28). Tailings reservoir disaster and environmental monitoring using the UAV-ground hyperspectral joint observation and processing: A case of study in xinjiang, the belt and road. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium IEEE, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898447"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107","DOI":"10.5194\/isprs-archives-XLIV-3-W1-2020-107-2020","article-title":"UAV image fast geocoding method for disaster scene monitoring","volume":"44","author":"Nho","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"012006","DOI":"10.1088\/1755-1315\/658\/1\/012006","article-title":"Analysis on the characteristics of cultivated land change in dianchi lake basin based on remote sensing image processing technonlogy in the past 20 years","volume":"658","author":"Gao","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_6","unstructured":"Tang, C.Y., Wu, Y.L., and Hor, M.K. (2008, January 12\u201315). Modified sift descriptor for image matching under interference. Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, Kunming, China."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yavariabdi, A., Kusetogullari, H., Celik, T., and Cicek, H. (2021). FastUAV-NET: A multi-uav detection algorithm for embedded platforms. Electronics, 10.","DOI":"10.3390\/electronics10060724"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yasein, M.S., and Agathoklis, P. (2008, January 18\u201321). A feature-based image registration technique for images of different scale. Proceedings of the IEEE International Symposium on Circuits & Systems, Seattle, WA, USA.","DOI":"10.1109\/ISCAS.2008.4542228"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/A:1008199403446","article-title":"Evaluation of interest point detectors","volume":"37","author":"Schmid","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","unstructured":"Bay, H., Ferrari, V., and Gool, L.V. (2005, January 20\u201325). Wide-baseline stereo matching with line segments. Proceedings of the IEEE Computer Society, San Diego, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alcantarilla, P.F., Bartoli, A., and Davison, A.J. (2012). KAZE Features, Springer. Computer Vision-ECCV.","DOI":"10.1007\/978-3-642-33783-3_16"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.12.003","article-title":"Line matching based on planar homography for stereo aerial images","volume":"104","author":"Sun","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2017.04.015","article-title":"Poor textural image tie point matching via graph theory","volume":"129","author":"Yuan","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.1109\/JSTARS.2021.3069919","article-title":"Comparison of keypoint detectors and descriptors for relative radiometric normalization of bitemporal remote sensing images","volume":"14","author":"Moghimi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","first-page":"1","article-title":"Distortion robust relative radiometric normalization of multitemporal and multisensor remote sensing images using image features","volume":"99","author":"Moghimi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201325). Object recognition from local scale-invariant features. Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra (Corfu), Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"88133","DOI":"10.1109\/ACCESS.2020.2989157","article-title":"Fast SIFT feature matching algorithm based on geometric transformation","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","unstructured":"Cui, S., Jiang, H., Wang, Z., and Shen, C. (2017, January 2\u20134). Application of neural network based on SIFT local feature extraction in medical image classification. Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC) IEEE, Chengdu, China."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, X., Gao, C., Qiu, X., Tian, Y., Zhu, Y., and Cao, W. (2019). Rapid mosaicking of unmanned aerial vehicle (UAV) images for crop growth monitoring using the SIFT algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11101226"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"057006","DOI":"10.1117\/1.3431688","article-title":"Converting color images to grayscale images by reducing dimensions","volume":"49","author":"Lee","year":"2010","journal-title":"Opt. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JEI.23.4.043004","article-title":"Color-to-grayscale conversion through weighted multiresolution channel fusion","volume":"23","author":"Wu","year":"2014","journal-title":"J. Electron. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/TPAMI.2005.188","article-title":"A performance evaluation of local descriptors","volume":"27","author":"Mikolajczyk","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Nister, D., and Stewenius, D. (2006, January 17\u201322). Scalable recognition with a vocabulary tree. Proceedings of the IEEE Conference on Computer Vision and Pattern Recogniton, New York, NY, USA."},{"key":"ref_25","unstructured":"Abdel-Hakim, A.E. (2006, January 17\u201322). CSIFT: A SIFT descriptor with color invariant characteristics. Proceedings of the CVPR\u201906, New York, NY, USA."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1109\/TCE.2010.5506003","article-title":"Panoramic video using scale-invariant feature transform with embedded color-invariant values","volume":"56","author":"Kwon","year":"2010","journal-title":"IEEE Trans. Consum. Electron."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2073-8994\/13\/8\/1380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:36:44Z","timestamp":1760164604000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2073-8994\/13\/8\/1380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,29]]},"references-count":27,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["sym13081380"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/sym13081380","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,7,29]]}}}