{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:51:26Z","timestamp":1778755886888,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:00:00Z","timestamp":1615420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-18-CE23-0023"],"award-info":[{"award-number":["ANR-18-CE23-0023"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.<\/jats:p>","DOI":"10.3390\/rs13061060","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T05:38:22Z","timestamp":1615441102000},"page":"1060","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1729-6162","authenticated-orcid":false,"given":"Luc","family":"Baudoux","sequence":"first","affiliation":[{"name":"University Gustave Eiffel, IGN-ENSG, LaSTIG, 73 Avenue de Paris, 94160 Saint-Mande, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6896-0049","authenticated-orcid":false,"given":"Jordi","family":"Inglada","sequence":"additional","affiliation":[{"name":"Centre d\u2019Etudes Spatiales de la Biosph\u00e8re, Universit\u00e9 de Toulouse, CNES\/CNRS\/IRD\/INRAE\/UPS, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2675-165X","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"Mallet","sequence":"additional","affiliation":[{"name":"University Gustave Eiffel, IGN-ENSG, LaSTIG, 73 Avenue de Paris, 94160 Saint-Mande, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1080\/01431161.2015.1093195","article-title":"An overview of 21 global and 43 regional land-cover mapping products","volume":"36","author":"Grekousis","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., White, J.C., Wulder, M.A., Hermosilla, T., Davidson, A.M., and Comber, A.J. (2020). Land cover harmonization using Latent Dirichlet Allocation. Int. J. Geogr. Inf. Sci., 1\u201327.","DOI":"10.1080\/13658816.2020.1796131"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.isprsjprs.2020.02.019","article-title":"Conterminous United States land cover change patterns 2001\u20132016 from the 2016 National Land Cover Database","volume":"162","author":"Homer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1080\/13658810410001705316","article-title":"Integrating land-cover data with different ontologies: Identifying change from inconsistency","volume":"18","author":"Comber","year":"2004","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1080\/13658810500072020","article-title":"Comparison of land cover maps using fuzzy agreement","volume":"19","author":"Fritz","year":"2005","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","first-page":"425","article-title":"Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe","volume":"9","author":"Neumann","year":"2007","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1080\/13658810500106729","article-title":"Using uncertain conceptual spaces to translate between land cover categories","volume":"19","author":"Ahlqvist","year":"2005","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1109\/TGRS.2006.871219","article-title":"A joint initiative for harmonization and validation of land cover datasets","volume":"44","author":"Herold","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","first-page":"102064","article-title":"Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa","volume":"88","author":"Rembold","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1111\/gcb.12838","article-title":"Mapping global cropland and field size","volume":"21","author":"Fritz","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/20964471.2019.1663627","article-title":"A global land cover map produced through integrating multi-source datasets","volume":"3","author":"Feng","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ruas, A. (2008). Map Generalization. Encyclopedia of GIS, Springer.","DOI":"10.1007\/978-0-387-35973-1_743"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, S., Chen, J., Zhang, X., and Xu, S. (2017). The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review. ISPRS Int. J. Geo. Inf., 6.","DOI":"10.3390\/ijgi6050154"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.cageo.2004.07.010","article-title":"Comparing categories among geographic ontologies","volume":"31","author":"Kavouras","year":"2005","journal-title":"Comput. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1023\/A:1006257630216","article-title":"Harmonisation and standardisation in multi-national environmental statistics\u2014mission impossible?","volume":"63","author":"Traub","year":"2000","journal-title":"Environ. Monit. Assess."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/17474230802332076","article-title":"Land-cover harmonisation and semantic similarity: Some methodological issues","volume":"3","author":"Jansen","year":"2008","journal-title":"J. Land Use Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/JSTARS.2015.2399509","article-title":"Super-Resolution Land Cover Mapping Based on Multiscale Spatial Regularization","volume":"8","author":"Hu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4951","DOI":"10.1109\/TGRS.2019.2894773","article-title":"Spatial\u2013Temporal Super-Resolution Land Cover Mapping With a Local Spatial\u2013Temporal Dependence Model","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Malkin, K., Robinson, C., Hou, L., Soobitsky, R., Czawlytko, J., Samaras, D., Saltz, J., Joppa, L., and Jojic, N. (2019). Label Super-Resolution Networks, ICLR."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1179\/caj.1966.3.1.10","article-title":"The Principles of Selection","volume":"3","author":"Pillewizer","year":"1966","journal-title":"Cartogr. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1559\/152304000783547993","article-title":"Knowledge Acquisition for Generalization Rules","volume":"27","year":"2000","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. (1976). A land Use and Land Cover Classification System for Use with Remote Sensor Data.","DOI":"10.3133\/pp964"},{"key":"ref_25","unstructured":"Xu, Q. (2016). Modelling Semantic Uncertainty of Land Classification System. [Ph.D. Thesis, The Hong Kong Polytechnic University]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.ecolind.2013.07.025","article-title":"Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system","volume":"36","author":"Kosmidou","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1016\/j.rse.2007.11.013","article-title":"Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets","volume":"112","author":"Herold","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1109\/TGRS.2018.2890404","article-title":"A Novel Approach to the Unsupervised Update of Land-Cover Maps by Classification of Time Series of Multispectral Images","volume":"57","author":"Paris","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10980-014-0028-9","article-title":"Expert knowledge for translating land cover\/use maps to General Habitat Categories (GHC)","volume":"29","author":"Adamo","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1109\/TSMCC.2009.2020689","article-title":"Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies","volume":"39","author":"Nguyen","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1080\/13658810210129120","article-title":"A method for the formalization and integration of geographical categorizations","volume":"16","author":"Kavouras","year":"2002","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, M.A., Egenhofer, M.J., and Rugg, R.D. (1999). Assessing Semantic Similarities among Geospatial Feature Class Definitions. Interoperating Geographic Information Systems, Springer.","DOI":"10.1007\/10703121_16"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0198-9715(03)00020-6","article-title":"Assessment of semantic similarity between land use\/land cover classification systems","volume":"28","author":"Feng","year":"2004","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_34","unstructured":"Di Gregorio, A. (2005). Land Cover Classification System: Classification Concepts and User Manual: LCCS, Food and Agriculture Organization of the United Nations. Chapter 2."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s10980-013-9863-3","article-title":"Translating land cover\/land use classifications to habitat taxonomies for landscape monitoring: A Mediterranean assessment","volume":"28","author":"Tomaselli","year":"2013","journal-title":"Landsc. Ecol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Arnold, S., Smith, G., Hazeu, G., Kosztra, B., Perger, C., Banko, G., Soukup, T., Strand, G.H., Sanz, N., and Bock, M. (2015). The EAGLE Concept: A Paradigm Shift in Land Monitoring. Land Use and Land Cover Semantics, CRC Press.","DOI":"10.1201\/b18746-7"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs5010019","article-title":"Harmonizing and Combining Existing Land Cover\/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale","volume":"5","author":"Vancutsem","year":"2012","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, J., Cao, X., Peng, S., and Ren, H. (2017). Analysis and Applications of GlobeLand30: A Review. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6080230"},{"key":"ref_39","unstructured":"Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., and Macherey, K. (2016). Google\u2019s Neural Machine Translation System: Bridging the Gap Between Human and Machine Translation. CoRR, Available online: http:\/\/xxx.lanl.gov\/abs\/1609.08144."},{"key":"ref_40","first-page":"196","article-title":"Comparison of large-area land cover products with national forest inventories and CORINE land cover in the European Alps","volume":"8","author":"Waser","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lu, M., Wu, W., You, L., Chen, D., Zhang, L., Yang, P., and Tang, H. (2017). A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics. Sensors, 17.","DOI":"10.3390\/s17071613"},{"key":"ref_42","unstructured":"Zhao, M., Hou, L., Le, H., Samaras, D., Jojic, N., Fassler, D., Kurc, T., Gupta, R., Malkin, K., and Kenneth, S. (2020). Label Super Resolution with Inter-Instance Loss. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Courtial, A., Ayedi, A.E., Touya, G., and Zhang, X. (2020). Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9050338"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I. (2017). Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series. Remote Sens., 9.","DOI":"10.3390\/rs9010095"},{"key":"ref_45","unstructured":"Heymann, Y. (1994). CORINE Land Cover: Technical Guide, European Commission, Directorate-General, Environment, Nuclear Safety and Civil Protection."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bechtel, B., Demuzere, M., and Stewart, I.D. (2019). A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sens., 11.","DOI":"10.3390\/rs11202420"},{"key":"ref_47","unstructured":"Moiret-Guigand, A., Jaffrain, G., Pennec, A., and Dufourmont, H. (2021). CLC2018 \/ CLCC1218 Validation Report, GMES Initial Operations\/Copernicus Land Monitoring Services. Technical Report."},{"key":"ref_48","first-page":"102221","article-title":"Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective","volume":"94","author":"Reinhart","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1080\/17538947.2016.1151956","article-title":"Assessing the suitability of GlobeLand30 for mapping land cover in Germany","volume":"9","author":"Arsanjani","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_50","first-page":"102","article-title":"Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales","volume":"78","author":"Vilar","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","article-title":"Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models","volume":"145","author":"Marcos","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.09.016","article-title":"Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture","volume":"158","author":"Ienco","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Pashaei, M., Kamangir, H., Starek, M.J., and Tissot, P. (2020). Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland. Remote Sens., 12.","DOI":"10.3390\/rs12060959"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0732-3123(96)90017-6","article-title":"Prime decomposition: Understanding uniqueness","volume":"15","author":"Zazkis","year":"1996","journal-title":"J. Math. Behav."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ardeshir, S., Zamir, A.R., Torroella, A., and Shah, M. (2014). GIS-Assisted Object Detection and Geospatial Localization, ECCV.","DOI":"10.1007\/978-3-319-10599-4_39"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Berg, T., Liu, J., Lee, S.W., Alexander, M.L., Jacobs, D.W., and Belhumeur, P.N. (2014). Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds, CVPR.","DOI":"10.1109\/CVPR.2014.259"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jiang, W., Knight, B.R., Cornelisen, C., Barter, P., and Kudela, R. (2017). Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters. Front. Mar. Sci., 4.","DOI":"10.3389\/fmars.2017.00151"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Aodha, O.M., Cole, E., and Perona, P. (2019). Presence-Only Geographical Priors for Fine-Grained Image Classification, ICCV.","DOI":"10.1109\/ICCV.2019.00969"},{"key":"ref_60","unstructured":"Chu, G., Potetz, B., Wang, W., Howard, A., Song, Y., Brucher, F., Leung, T., and Adam, H. (November, January 27). Geo-Aware Networks for Fine-Grained Recognition. Proceedings of the ICCV Workshop, Seoul, Korea."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1109\/TMM.2015.2436057","article-title":"Tag Features for Geo-Aware Image Classification","volume":"17","author":"Liao","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Tang, K., Paluri, M., Fei-Fei, L., Fergus, R., and Bourdev, L. (2015). Improving Image Classification with Location Context, ICCV.","DOI":"10.1109\/ICCV.2015.121"},{"key":"ref_63","unstructured":"Sakai, M., Homma, N., Gupta, M., and Abe, K. (2002, January 27\u201329). Statistical approximation learning of discontinuous functions using simultaneous recurrent neural networks. Proceedings of the IEEE Internatinal Symposium on Intelligent Control, Monterey, CA, USA."},{"key":"ref_64","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. NIPS. arXiv."},{"key":"ref_65","unstructured":"Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., and Tran, D. (2018, January 10\u201315). Image Transformer. Proceedings of the Machine Learning Research, PMLR, Stockholm, Sweden."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Salehi, S.S.M., Erdogmus, D., and Gholipour, A. (2017). Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. Machine Learning in Medical Imaging, Springer International Publishing.","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","article-title":"The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling","volume":"8","author":"Ho","year":"2020","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017). Focal Loss for Dense Object Detection, ICCV.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, IEEE. 3DV.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","article-title":"Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers","volume":"51","author":"Khened","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S. (2019). nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. Informatik Aktuell, Springer Fachmedien Wiesbaden.","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wong, K.C.L., Moradi, M., Tang, H., and Syeda-Mahmood, T. (2018). 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-00931-1_70"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/LSP.2010.2042516","article-title":"New Method for Performance Evaluation of Grayscale Image Denoising Filters","volume":"17","author":"Russo","year":"2010","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_76","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_77","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/83.585239","article-title":"Image enhancement based on a nonlinear multiscale method","volume":"6","author":"Sattar","year":"1997","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1060\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:33:57Z","timestamp":1760160837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1060"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,11]]},"references-count":77,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061060"],"URL":"https:\/\/doi.org\/10.3390\/rs13061060","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,11]]}}}