{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T15:13:05Z","timestamp":1760800385354,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61671408"],"award-info":[{"award-number":["61671408"]}]},{"name":"the Joint Fund Project of Chinese Ministry of Education","award":["6141A02022350","6141A02022362"],"award-info":[{"award-number":["6141A02022350","6141A02022362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods.<\/jats:p>","DOI":"10.3390\/rs12183066","type":"journal-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T07:06:09Z","timestamp":1600499169000},"page":"3066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization"],"prefix":"10.3390","volume":"12","author":[{"given":"Shuhan","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"Xue","sequence":"additional","affiliation":[{"name":"Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7825-0902","authenticated-orcid":false,"given":"Han","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9276-8679","authenticated-orcid":false,"given":"Liaoying","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5450-4891","authenticated-orcid":false,"given":"Chein-I","family":"Chang","sequence":"additional","affiliation":[{"name":"Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/S0262-8856(03)00137-9","article-title":"Image registration methods: A survey","volume":"21","author":"Flusser","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"4516","DOI":"10.1109\/TGRS.2011.2144607","article-title":"Uniform robust scale-invariant feature matching for optical remote sensing images","volume":"49","author":"Sedaghat","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/LGRS.2016.2582528","article-title":"Remote sensing optical image registration using modified uniform robust SIFT","volume":"13","author":"Paul","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/TGRS.2018.2790483","article-title":"OS-SIFT: A robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas","volume":"56","author":"Xiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/LGRS.2019.2899123","article-title":"Remote sensing image registration based on modified SIFT and feature slope grouping","volume":"16","author":"Chang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.isprsjprs.2017.11.019","article-title":"Uniform competency-based local feature extraction for remote sensing images","volume":"135","author":"Sedaghat","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"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":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TPAMI.2009.77","article-title":"DAISY: An efficient dense descriptor applied to wide-baseline stereo","volume":"32","author":"Tola","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TGRS.2015.2420659","article-title":"Remote sensing image matching based on adaptive binning SIFT descriptor","volume":"53","author":"Sedaghat","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1109\/TBME.2010.2042169","article-title":"A partial intensity invariant feature descriptor for multimodal retinal image registration","volume":"57","author":"Chen","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.isprsjprs.2015.06.003","article-title":"Distinctive order based self-similarity descriptor for multi-Sensor remote sensing image matching","volume":"108","author":"Sedaghat","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2019.04.018","article-title":"Illumination-robust remote sensing image matching based on oriented self-similarity","volume":"153","author":"Sedaghat","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","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":"1980","journal-title":"Commun. ACM"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4895","DOI":"10.1109\/TGRS.2013.2285814","article-title":"A novel image registration algorithm for remote sensing under affine transformation","volume":"52","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2014.2325970","article-title":"A novel point-matching algorithm based on fast sample consensus for image registration","volume":"12","author":"Wu","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","unstructured":"Ma, J.Y., Zhao, J., Jiang, J.J., and Zhou, H.B. (2017, January 19\u201325). Locality preserving matching. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, VIC, Australia.","DOI":"10.24963\/ijcai.2017\/627"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"24568","DOI":"10.1109\/ACCESS.2017.2768078","article-title":"Robust image feature matching via progressive sparse spatial consensus","volume":"5","author":"Ma","year":"2017","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1109\/42.563664","article-title":"Multimodality image registration by maximization of mutual information","volume":"16","author":"Maes","year":"1997","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/el:20082477","article-title":"Multi-spectral remote image registration based on SIFT","volume":"44","author":"Yi","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TGRS.2013.2242895","article-title":"Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric","volume":"52","author":"Liang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"596","article-title":"Image similarity using mutual information of regions","volume":"3023","author":"Russakoff","year":"2004","journal-title":"Eur. Conf. Comput. Vis."},{"key":"ref_26","unstructured":"Li, L.X. (2013). The Registration and Fusion of Multi-sensor Images Based on Mutual Information. [Master\u2019s Thesis, University of Electronic Science and Technology of China]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TGRS.2007.892601","article-title":"ARRSI: Automatic registration of remote sensing images","volume":"45","author":"Wong","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.patrec.2005.08.008","article-title":"Matching two clusters of points extracted from satellite images","volume":"27","author":"Navy","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rueckert, D., Clarkson, M.J., Hill, D.L.G., and Hawkes, D.J. (2000, January 14). Non-rigid registration using higher-order mutual information. Proceedings of the Medical Imaging 2000: Image Processing Proceedings SPIE, San Diego, CA, USA.","DOI":"10.1117\/12.804801"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/42.876307","article-title":"Image registration by maximization of combined mutual information and gradient information","volume":"19","author":"Pluim","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1109\/TIP.2003.819237","article-title":"Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient","volume":"12","author":"Johnson","year":"2003","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TGRS.2007.892599","article-title":"Robust multispectral image registration using mutual-information models","volume":"45","author":"Kern","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1109\/TGRS.2003.817664","article-title":"Performance of mutual information similarity measure for registration of multitemporal remote sensing images","volume":"41","author":"Chen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/S0031-3203(98)00091-0","article-title":"An overlap invariant entropy measure of 3D medical image alignment","volume":"32","author":"Studholme","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1117\/12.428251","article-title":"Mutual information as a similarity measure for remote sensing image registration","volume":"4383","author":"Johnson","year":"2001","journal-title":"Proc. SPIE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TGRS.2013.2281391","article-title":"A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information","volume":"52","author":"Gong","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"124204","DOI":"10.7498\/aps.64.124204","article-title":"Multi-source remote sensing image registration based on scale-invariant feature transform and optimization of regional mutual information","volume":"64","author":"Zhao","year":"2015","journal-title":"Acta Phys. Sin. Chin. Ed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1080\/01431161.2018.1437295","article-title":"Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information","volume":"39","author":"Chen","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2014.01.009","article-title":"A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences","volume":"90","author":"Ye","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, X.R., Zhao, L.Y., and Chen, S.H. (2019). A novel coarse-to-fine scheme for remote sensing image registration based on SIFT and phase correlation. Remote Sens., 11.","DOI":"10.3390\/rs11151833"},{"key":"ref_41","unstructured":"Chen, S.H., Zhong, S.W., Xue, B., Li, X.R., Zhao, L.Y., and Chang, C.-I. (2020). Iterative Scale-Invariant Feature Transform for Remote Sensing Image Registration. IEEE Trans. Geosci. Remote Sens., 1\u201322."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gharbia, R., Ahmed, S.A., and Hassanien, A.E. (2015). Remote Sensing Image Registration Based on Particle Swarm Optimization and Mutual Information, Springer.","DOI":"10.1007\/978-81-322-2247-7_41"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ijleo.2015.09.199","article-title":"Infrared and visual image registration based on mutual information with a combined particle swarm optimization Powell search algorithm","volume":"127","author":"Zhuang","year":"2016","journal-title":"Opt. Int. J. Light Electron. Opt."},{"key":"ref_44","first-page":"392","article-title":"Fast Algorithm for Simulated Annealing Applied to Registering of Noisy Images","volume":"9","author":"Xia","year":"2003","journal-title":"J. Shanghai Univ."},{"key":"ref_45","first-page":"53","article-title":"Research on IR and Visual Image Registration Based on Mutual Information and Ant Colony Algorithm","volume":"29","author":"Liu","year":"2008","journal-title":"Microcomput. App."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3428","DOI":"10.1016\/j.eswa.2008.02.072","article-title":"Applying particle swarm optimization algorithm to roundness measurement","volume":"36","author":"Sun","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_47","unstructured":"Sun, J., Feng, B., and Xu, W.B. (2004, January 19\u201323). Particle swarm optimization with particles having quantum behavior. Proceedings of the 2004 Congress on Evolutionary Computation CEC2004, Portland, OR, USA."},{"key":"ref_48","unstructured":"Sun, J., Feng, B., and Xu, W.B. (2005, January 10\u201312). Adaptive Parameter Control for Quantum-behaved Particle Swarm Optimization on Individual Level. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Hawaii, HI, USA."},{"key":"ref_49","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic\/Plenum Publishers."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/18.857802","article-title":"An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis","volume":"46","author":"Chang","year":"2000","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_51","unstructured":"(2014, October 15). ENVI Image Analysis Software (5.2 version). L3HARRIS Geospatial. Available online: https:\/\/www.harrisgeospatial.com\/Software-Technology\/ENVI."},{"key":"ref_52","unstructured":"(2018, December 05). UC Merced Land Use Dataset. Available online: http:\/\/140.112.27.140\/wp-content\/uploads\/2018\/12\/Datasets.zip."},{"key":"ref_53","unstructured":"(2010, October 28). High-Resolution Orthoimagery Data. Available online: http:\/\/weegee.vision.ucmerced.edu\/datasets\/landuse.html."},{"key":"ref_54","unstructured":"USGS (2016, August 28). Earth Explorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_56","unstructured":"(2018, September 29). Matlab Code of LLT. Available online: https:\/\/github.com\/jiayi-ma\/LLT."},{"key":"ref_57","unstructured":"(2019, March 16). Matlab Code of LPM. Available online: https:\/\/github.com\/jiayi-ma\/LPM."},{"key":"ref_58","unstructured":"(2018, December 31). Matlab Code of PSSC. Available online: https:\/\/github.com\/JiahaoPlus\/PSSC."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3066\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:33Z","timestamp":1760177493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/3066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,19]]},"references-count":58,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12183066"],"URL":"https:\/\/doi.org\/10.3390\/rs12183066","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,9,19]]}}}