{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:06:00Z","timestamp":1760231160039,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T00:00:00Z","timestamp":1661558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Academy of Sciences","award":["CX-262"],"award-info":[{"award-number":["CX-262"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image\u2019s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.<\/jats:p>","DOI":"10.3390\/rs14174228","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"4228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching"],"prefix":"10.3390","volume":"14","author":[{"given":"Huangwei","family":"Tu","sequence":"first","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"given":"Yu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9162-7367","authenticated-orcid":false,"given":"Changpei","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2584","DOI":"10.1109\/TIP.2022.3157450","article-title":"Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter","volume":"31","author":"Yao","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","article-title":"A review of multimodal image matching: Methods and applications","volume":"73","author":"Jiang","year":"2021","journal-title":"Inform. Fusion"},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","unstructured":"Hisham, M.B., Yaakob, S.N., Raof, R.A., Nazren, A.A., and Wafi, N.M. (2015, January 13\u201314). Template matching using sum of squared difference and normalized cross correlation. Proceedings of the 2015 IEEE Student Conference on Research and Development (SCOReD), Kuala Lumpur, Malysia.","DOI":"10.1109\/SCORED.2015.7449303"},{"key":"ref_7","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_8","first-page":"317","article-title":"Matching by tone mapping: Photometric invariant template matching","volume":"36","author":"David","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Kuglin, C.D. (1975, January 26\u201328). The phase correlation image alignment methed. Proceedings of the International Conference on Cybernetics and Society\/IEEE Systems, Man, and Cybernetics Society, New York, NY, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6451","DOI":"10.1109\/TGRS.2020.2976865","article-title":"OS-PC: Combining feature representation and 3-D phase correlation for subpixel optical and SAR image registration","volume":"58","author":"Xiang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-annals-III-1-9-2016","article-title":"Hopc: A novel similarity metric based on geometric structural properties for multi-modal remote sensing image matching","volume":"3","author":"Ye","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s004260000024","article-title":"Phase congruency: A low-level image invariant","volume":"64","author":"Kovesi","year":"2000","journal-title":"Psychol. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9059","DOI":"10.1109\/TGRS.2019.2924684","article-title":"Fast and robust matching for multimodal remote sensing image registration","volume":"57","author":"Ye","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fan, Z., Zhang, L., Liu, Y., Wang, Q., and Zlatanova, S. (2021). Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images. Remote Sens., 13.","DOI":"10.3390\/rs13173535"},{"key":"ref_15","first-page":"2","article-title":"Sift-the scale invariant feature transform","volume":"2","author":"Lowe","year":"2004","journal-title":"Int. J."},{"key":"ref_16","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, 2004 CVPR 2004, Washington, DC, USA."},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-like algorithm for SAR images","volume":"53","author":"Dellinger","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/LGRS.2016.2600858","article-title":"Remote sensing image registration with modified SIFT and enhanced feature matching","volume":"14","author":"Ma","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Gao, C., Li, W., Tao, R., and Du, Q. (arXiv, 2022). MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration, arXiv, preprint.","DOI":"10.1109\/TGRS.2022.3193109"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5368","DOI":"10.1109\/TGRS.2018.2815523","article-title":"SAR and optical image registration using nonlinear diffusion and phase congruency structural descriptor","volume":"56","author":"Fan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2018.06.010","article-title":"A local phase based invariant feature for remote sensing image matching","volume":"142","author":"Ye","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","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":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","first-page":"1727","article-title":"Heterologous Images Matching Considering Anisotropic Weighted Moment and Absolute Phase Orientation","volume":"46","author":"Ao","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Fan, Z., Liu, Y., Liu, Y., Zhang, L., Zhang, J., Sun, Y., and Ai, H. (2022). 3MRS: An Effective Coarse-to-Fine Matching Method for Multimodal Remote Sensing Imagery. Remote Sens., 143.","DOI":"10.3390\/rs14030478"},{"key":"ref_30","first-page":"1","article-title":"LPSO: Multi-source image matching considering the description of local phase sharpness orientation","volume":"14","author":"Yang","year":"2022","journal-title":"IEEE Photonics J."},{"key":"ref_31","first-page":"1","article-title":"Robust Feature Matching via Local Consensus","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Li, Z. (2013). Research on Iris Recognition Algorithm Based on 2D Log-Gabor Wavelet. [Ph.D. Thesis, Northeastern University]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1109\/TGRS.2017.2656380","article-title":"Robust registration of multimodal remote sensing images based on structural similarity","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","unstructured":"Kovesi, P. (2003, January 10\u201312). Phase congruency detects corners and edges. Proceedings of the Seventh International Conference on Digital Image Computing: Techniques and Applications, Sydney, Australia."},{"key":"ref_35","unstructured":"Horn, B., Klaus, B., and Horn, P. (1986). Robot Vision, MIT Press."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. Machine learning for high-speed corner detection. Proceedings of the European Conference on Computer Vision, Graz, Austria, 7\u201313 May 2006.","DOI":"10.1007\/11744023_34"},{"key":"ref_37","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":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","first-page":"1","article-title":"LNIFT: Locally Normalized Image for Rotation Invariant Multimodal Feature Matching","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, X., Zhao, L., and Chen, S. (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_40","doi-asserted-by":"crossref","unstructured":"Rasmy, L., Sebari, I., and Ettarid, M. (2021). Automatic sub-pixel co-registration of remote sensing images using phase correlation and Harris detector. Remote Sens., 13.","DOI":"10.3390\/rs13122314"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12559-020-09724-6","article-title":"Robust visual saliency optimization based on bidirectional Markov chains","volume":"13","author":"Jiang","year":"2021","journal-title":"Cogn. Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:16:26Z","timestamp":1760141786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,27]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174228"],"URL":"https:\/\/doi.org\/10.3390\/rs14174228","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,27]]}}}