{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:31:33Z","timestamp":1780597893762,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"research startup fund at Sun Yat-sen University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The multi-scale representation of remote sensing images is crucial for information extraction, data analysis, and image processing. However, traditional methods such as image pyramid and image filtering often result in the loss of image details, particularly edge information, during the simplification and merging processes at different scales and resolutions. Furthermore, when applied to coastal landforms with rich texture features, such as biologically diverse areas covered with vegetation, these methods struggle to preserve the original texture characteristics. In this study, we propose a new method, multi-scale expression of coastal landforms considering texture features (METF-C), based on computer vision techniques. This method combines superpixel segmentation and texture transfer technology to improve the multi-scale representation of coastal landforms in remote sensing images. First, coastal landform elements are segmented using superpixel technology. Then, global merging is performed by selecting different classes of superpixels, with boundaries smoothed using median filtering and morphological operators. Finally, texture transfer is applied to create a fusion image that maintains both scale and level consistency. Experimental results demonstrate that METF-C outperforms traditional methods by effectively simplifying images while preserving important geomorphic features and maintaining global texture information across multiple scales. This approach offers significant improvements in edge preservation and texture retention, making it a valuable tool for analyzing coastal landforms in remote sensing imagery.<\/jats:p>","DOI":"10.3390\/rs16203862","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T08:56:32Z","timestamp":1729155392000},"page":"3862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Scale Expression of Coastal Landform in Remote Sensing Images Considering Texture Features"],"prefix":"10.3390","volume":"16","author":[{"given":"Ruojie","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"},{"name":"School of Software Engineering, Henan University of Economics and Law, Zhengzhou 450016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9699-8551","authenticated-orcid":false,"given":"Yilang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast Feature Pyramids for Object Detection","volume":"36","author":"Appel","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0924-2716(02)00162-4","article-title":"A Comparison of Three Image-Object Methods for the Multiscale Analysis of Landscape Structure","volume":"57","author":"Hay","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0034-4257(81)90033-X","article-title":"Texture Transforms of Remote Sensing Data","volume":"11","author":"Irons","year":"1981","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4982","DOI":"10.1109\/JSTARS.2018.2881342","article-title":"Remote Sensing Image Fusion Using Hierarchical Multimodal Probabilistic Latent Semantic Analysis","volume":"11","author":"Haut","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3233\/ICA-2005-12201","article-title":"A Multiscale Approach to Pixel-Level Image Fusion","volume":"12","author":"He","year":"2005","journal-title":"Integr. Comput.-Aided Eng."},{"key":"ref_8","first-page":"1607","article-title":"Evaluation of Beach Hydromorphological Behaviour and Classification Using Image Classification Techniques","volume":"II","author":"Teodoro","year":"2009","journal-title":"J. Coast. Res."},{"key":"ref_9","first-page":"955","article-title":"Morphological Change Characteristics of Tidal Gullies in Central Jiangsu Coast","volume":"68","author":"Wu","year":"2013","journal-title":"Acta Geograph. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/83.388080","article-title":"Nonlinear Multivariate Image Filtering Techniques","volume":"4","author":"Tang","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","unstructured":"Gluckman, J. (2006, January 17\u201322). Scale Variant Image Pyramids. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA."},{"key":"ref_12","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice Hall."},{"key":"ref_13","first-page":"48","article-title":"Comparative Study of Commonly Used Nonlinear Filtering Methods","volume":"16","author":"Wei","year":"2009","journal-title":"Electro-Optics Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/78.978374","article-title":"A Tutorial on Particle Filters for Online Nonlinear\/Non-Gaussian Bayesian Tracking","volume":"50","author":"Arulampalam","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","first-page":"443","article-title":"SUSAN\u2014A New Approach to Low Level Image Processing","volume":"21","author":"Smith","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","article-title":"The Laplacian Pyramid as a Compact Image Code","volume":"31","author":"Burt","year":"1983","journal-title":"IEEE Trans. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1080\/13658816.2022.2141751","article-title":"Automatic Measurement of Building Setbacks and Streetscape Widths and Their Spatial Variability Along Streets and in Plots: Integration of Streetscape Skeletons and Plot Geometry","volume":"37","author":"Usui","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.compenvurbsys.2019.01.009","article-title":"A Polygon Aggregation Method with Global Feature Preservation Using Superpixel Segmentation","volume":"75","author":"Shen","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.landurbplan.2012.02.008","article-title":"Assessing Contextual Descriptive Features for Plot-Based Classification of Urban Areas","volume":"106","author":"Hermosilla","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_20","first-page":"59","article-title":"Exploring Geographic Information Systems (Book Review)","volume":"40","author":"Burger","year":"1998","journal-title":"Geogr. Bull."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1007\/s00158-019-02449-7","article-title":"Layout Optimization of Simplified Trusses Using Mixed Integer Linear Programming with Runtime Generation of Constraints","volume":"61","author":"Fairclough","year":"2020","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_22","first-page":"1","article-title":"Finding Optimal Sequences for Area Aggregation\u2014A* vs. Integer Linear Programming","volume":"7","author":"Peng","year":"2020","journal-title":"ACM Trans. Spat. Algorithms Syst. (TSAS)"},{"key":"ref_23","first-page":"5015","article-title":"Coastal Line Simplification Based on Fuzzy Logic","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1698","article-title":"Multi-Scale Analysis of Coastal Landforms Using Feature Extraction Techniques","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"5307","article-title":"Automatic Coastal Landform Merging Using Convolutional Neural Networks","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","first-page":"27","article-title":"A Conceptual Model of the Interaction between Simplification and Smoothing","volume":"23","author":"McMaster","year":"1986","journal-title":"Cartogr. J."},{"key":"ref_27","first-page":"435","article-title":"Simplifying a Polygonal Subdivision While Keeping It Simple","volume":"44","author":"Buchin","year":"2011","journal-title":"Comput. Geom."},{"key":"ref_28","first-page":"323","article-title":"A Scale-Adaptive Model for the Simplification of Areal Features","volume":"16","author":"Tong","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","first-page":"5447","article-title":"Multi-Resolution Terrain Simplification and Visualization Based on Triangulated Irregular Networks","volume":"5","author":"Gong","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.geomorph.2015.10.005","article-title":"Appropriate Complexity for the Prediction of Coastal and Estuarine Geomorphic Behaviour at Decadal to Centennial Scales","volume":"256","author":"French","year":"2016","journal-title":"Geomorphology"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, Y.J., Yu, C.C., Yu, M.J., and Su, D. (2016, January 27\u201330). Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.77"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1007\/s10851-016-0694-0","article-title":"Median Filtering: A New Insight","volume":"58","author":"Villar","year":"2017","journal-title":"J. Math. Imaging Vis."},{"key":"ref_34","first-page":"9","article-title":"Image Restoration Based on Morphological Operations","volume":"4","author":"Raid","year":"2014","journal-title":"Int. J. Comput. Sci. Eng. Inf. Technol."},{"key":"ref_35","unstructured":"Wang, Z., Zhao, L., Chen, H., and Lu, S. (March, January 22). Texture Reformer: Towards Fast and Universal Interactive Texture Transfer. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"402","DOI":"10.2307\/212259","article-title":"Map of Coastal Landforms of the World","volume":"48","author":"McGill","year":"1958","journal-title":"Geogr. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_38","first-page":"13","article-title":"Overview of Corrosion and Its Control: A Critical Review","volume":"3","author":"Harsimran","year":"2021","journal-title":"Proc. Eng. Sci."},{"key":"ref_39","first-page":"120","article-title":"Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising","volume":"12","author":"Zhang","year":"2023","journal-title":"Appl. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, Z., Lin, Z., Qi, H., Wang, L., and Yang, Y. (2019, January 16\u201320). Image Super-Resolution by Neural Texture Transfer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00817"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1109\/TNET.2020.2988047","article-title":"Optimal Submarine Cable Path Planning and Trunk-and-Branch Tree Network Topology Design","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e17026","DOI":"10.1002\/aic.17026","article-title":"A Survey of Multiscale Modeling: Foundations, Historical Milestones, Current Status, and Future Prospects","volume":"67","author":"Radhakrishnan","year":"2021","journal-title":"AIChE J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s11045-021-00802-y","article-title":"Two-Step Non-Local Means Method for Image Denoising","volume":"33","author":"Zhang","year":"2022","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1007\/s11390-008-9129-8","article-title":"A Robust and Fast Non-Local Means Algorithm for Image Denoising","volume":"23","author":"Liu","year":"2008","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_45","first-page":"1716","article-title":"Multiresponse Robust Design: Mean Square Error (MSE) Criterion","volume":"175","year":"2006","journal-title":"Appl. Math. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_47","unstructured":"Esri, Inc. (2011). ArcGIS Desktop: Release 10, Environmental Systems Research Institute."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","article-title":"Normalized Mutual Information Feature Selection","volume":"20","author":"Tesmer","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neucom.2016.01.004","article-title":"Kernel least mean square with adaptive kernel size","volume":"191","author":"Chen","year":"2016","journal-title":"Neurocomputing."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1016\/j.ins.2011.02.008","article-title":"A study on scale factor in distributed differential evolution","volume":"181","author":"Weber","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1785\/0220120087","article-title":"What is sigma of the stress drop?","volume":"84","author":"Cotton","year":"2013","journal-title":"Seismol. Res. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yoo, D., Park, S., Lee, J.Y., Paek, A., and Kweon, I.S. (2015, January 7\u201312). Multi-scale pyramid pooling for deep convolutional representation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301274"},{"key":"ref_53","first-page":"102","article-title":"Multi-scale and multi-level image processing for feature preservation","volume":"200","author":"Zhang","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_54","first-page":"2365","article-title":"Texture synthesis and transfer with deep learning: A review","volume":"27","author":"Gong","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3862\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:15:15Z","timestamp":1760112915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":54,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203862"],"URL":"https:\/\/doi.org\/10.3390\/rs16203862","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,17]]}}}