{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T20:42:51Z","timestamp":1767991371128,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agencia Estatal de Investigaci\u00f3n, Spain","award":["CTM2016-77733-R"],"award-info":[{"award-number":["CTM2016-77733-R"]}]},{"name":"Ministerio de Ciencias e Innovaci\u00f3n, Spain","award":["PID2020-117142GB-I0"],"award-info":[{"award-number":["PID2020-117142GB-I0"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m\/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m\/pixel bands and the other for 60 m\/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.<\/jats:p>","DOI":"10.3390\/rs13245007","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"5007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4048-8330","authenticated-orcid":false,"given":"Luis","family":"Salgueiro","sequence":"first","affiliation":[{"name":"Department of Signal Theory and Communications, Universitat Polit\u00e8cnica de Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9646-1017","authenticated-orcid":false,"given":"Javier","family":"Marcello","sequence":"additional","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global, IOCAG, Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-9961","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"Vilaplana","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications, Universitat Polit\u00e8cnica de Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","unstructured":"Copernicus Open Access Hub (2021, March 21). European Space Agency. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, R., Cavallaro, G., and Jitsev, J. (October, January 26). Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323734"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Alparone, L., Aiazzi, B., Baronti, S., and Garzelli, A. (2015). Remote Sensing Image Fusion, CRC Press.","DOI":"10.1201\/b18189"},{"key":"ref_6","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2015). Remote Sensing and Image Interpretation, John Wiley & Sons."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"883","DOI":"10.5194\/isprs-archives-XLI-B3-883-2016","article-title":"Single-image super resolution for multispectral remote sensing data using convolutional neural networks","volume":"41","author":"Liebel","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wagner, L., Liebel, L., and K\u00f6rner, M. (2019). Deep residual learning for single-image super-resolution of multi-spectral satellite imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci., 4.","DOI":"10.5194\/isprs-annals-IV-2-W7-189-2019"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., and N\u00f5ges, T. (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sens., 8.","DOI":"10.3390\/rs8080640"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kolokoussis, P., and Karathanassi, V. (2018). Oil spill detection and mapping using sentinel 2 imagery. J. Mar. Sci. Eng., 6.","DOI":"10.3390\/jmse6010004"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 data for land cover\/use mapping: A review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pedrayes, O.D., Lema, D.G., Garc\u00eda, D.F., Usamentiaga, R., and Alonso, \u00c1. (2021). Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13122292"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3390462","article-title":"A deep journey into super-resolution: A survey","volume":"53","author":"Anwar","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_14","unstructured":"Arefin, M.R., Michalski, V., St-Charles, P.L., Kalaitzis, A., Kim, S., Kahou, S.E., and Bengio, Y. (2020, January 14\u201319). Multi-image super-resolution for remote sensing using deep recurrent networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., and Tsakalides, P. (2019). Survey of deep-learning approaches for remote sensing observation enhancement. Sensors, 19.","DOI":"10.3390\/s19183929"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhu, X., Xu, Y., and Wei, Z. (August, January 28). Super-Resolution of Sentinel-2 Images Based on Deep Channel-Attention Residual Network. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897860"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salgueiro Romero, L., Marcello, J., and Vilaplana, V. (2020). Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks. Remote Sens., 12.","DOI":"10.3390\/rs12152424"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, C., Zhang, J., Liu, J., Zhang, C., Fei, R., and Xu, S. (2020). PercepPan: Towards unsupervised pan-sharpening based on perceptual loss. Remote Sens., 12.","DOI":"10.3390\/rs12142318"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kaplan, G. (2018). Sentinel-2 Pan Sharpening\u2014Comparative Analysis. Proceedings, 2.","DOI":"10.3390\/ecrs-2-05158"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"723","DOI":"10.5194\/isprs-archives-XLI-B7-723-2016","article-title":"Pansharpening on the narrow VNIR and SWIR spectral bands of Sentinel-2","volume":"41","author":"Vaiopoulos","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1080\/01431161.2017.1392640","article-title":"The effect of fusing Sentinel-2 bands on land-cover classification","volume":"39","author":"Jogun","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Armannsson, S.E., Ulfarsson, M.O., Sigurdsson, J., Nguyen, H.V., and Sveinsson, J.R. (2021). A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald\u2019s Protocol and Bayesian Optimization. Remote Sens., 13.","DOI":"10.3390\/rs13112192"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111679","DOI":"10.1016\/j.rse.2020.111679","article-title":"A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard","volume":"240","author":"Brook","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2016.10.030","article-title":"Fusion of Sentinel-2 images","volume":"187","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4610","DOI":"10.1109\/TGRS.2017.2694881","article-title":"Super-resolving multiresolution images with band-independent geometry of multispectral pixels","volume":"55","author":"Brodu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, K., Sumbul, G., and Demir, B. (2020, January 9\u201311). An Approach To Super-Resolution Of Sentinel-2 Images Based On Generative Adversarial Networks. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105165"},{"key":"ref_28","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial networks. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Palsson, F., Sveinsson, J.R., and Ulfarsson, M.O. (2018). Sentinel-2 image fusion using a deep residual network. Remote Sens., 10.","DOI":"10.3390\/rs10081290"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 22\u201325). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gargiulo, M., Mazza, A., Gaetano, R., Ruello, G., and Scarpa, G. (2019). Fast super-resolution of 20 m Sentinel-2 bands using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11222635"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, J., He, Z., and Hu, J. (2020). Sentinel-2 Sharpening via parallel residual network. Remote Sens., 12.","DOI":"10.3390\/rs12020279"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Change Loy, C. (2018, January 8\u201314). Esrgan: Enhanced super-resolution generative adversarial networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_37","unstructured":"(2021, November 26). MultiSpectral Instrument (MSI) Overview. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/technical-guides\/sentinel-2-msi\/msi-instrument."},{"key":"ref_38","unstructured":"(2021, March 21). Sentinel-2 User Handbook. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_39","first-page":"691","article-title":"Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images","volume":"63","author":"Wald","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20199). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, X., Liu, Y., and Zeng, Q. (2019). Generative adversarial networks capabilities for super-resolution reconstruction of weather radar echo images. Atmosphere, 10.","DOI":"10.3390\/atmos10090555"},{"key":"ref_42","unstructured":"Romero, L.S., Marcello, J., and Vilaplana, V. (2019, January 16\u201318). Comparative study of upsampling methods for super-resolution in remote sensing. Proceedings of the Twelfth International Conference on Machine Vision (ICMV 2019), Amsterdam, The Netherlands."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_44","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_45","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992, January 1\u20135). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proceedings of the Summaries 3rd Annu. JPL Airborne Geosci Workshop, Pasadena, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","unstructured":"Wald, L. (2002). Data Fusion: Definitions and Architectures: Fusion of Images of Different Spatial Resolutions, Presses des MINES."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-02247-0","article-title":"Remote sensing image processing","volume":"5","author":"Tuia","year":"2011","journal-title":"Synth. Lect. Image Video Multimed. Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Signoroni, A., Savardi, M., Baronio, A., and Benini, S. (2019). Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging, 5.","DOI":"10.3390\/jimaging5050052"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep learning for hyperspectral image classification: An overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Moliner, E., Romero, L.S., and Vilaplana, V. (2020, January 4\u20138). Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging. Proceedings of the ICASSP 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053384"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1080\/15481603.2018.1502910","article-title":"Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery","volume":"56","author":"Marcello","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine vs. Random Forest for Remote Sensing Image Classification: A Meta-analysis and systematic review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"L20405","DOI":"10.1029\/2007GL031021","article-title":"NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing","volume":"34","author":"Wang","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/JSTARS.2018.2855564","article-title":"Modeling winter wheat leaf area index and canopy water content with three different approaches using Sentinel-2 multispectral instrument data","volume":"12","author":"Pan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Pereira-Pires, J.E., Aubard, V., Ribeiro, R.A., Fonseca, J.M., Silva, J., and Mora, A. (2020). Semi-automatic methodology for fire break maintenance operations detection with Sentinel-2 imagery and artificial neural network. Remote Sens., 12.","DOI":"10.3390\/rs12060909"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cucca, B., Recanatesi, F., and Ripa, M.N. (2020). Evaluating the Potential of Vegetation Indices in Detecting Drought Impact Using Remote Sensing Data in a Mediterranean Pinewood. International Conference on Computational Science and Its Applications, Springer.","DOI":"10.1007\/978-3-030-58814-4_4"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TGRS.2019.2940826","article-title":"Red-edge band vegetation indices for leaf area index estimation from Sentinel-2\/msi imagery","volume":"58","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lin, S., Li, J., Liu, Q., Li, L., Zhao, J., and Yu, W. (2019). Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from Sentinel-2 to estimate gross primary productivity. Remote Sens., 11.","DOI":"10.3390\/rs11111303"},{"key":"ref_62","first-page":"100283","article-title":"Red-Edge Normalised Difference Vegetation Index NDVI705 from Sentinel-2 imagery to assess post-fire regeneration","volume":"17","author":"Evangelides","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6536","DOI":"10.1109\/ACCESS.2020.3046657","article-title":"Advanced Processing of Multiplatform Remote Sensing Imagery for the Monitoring of Coastal and Mountain Ecosystems","volume":"9","author":"Marcello","year":"2020","journal-title":"IEEE Access"},{"key":"ref_64","unstructured":"IEO (Instituto Espa\u00f1ol de Oceanograf\u00eda) (2021, April 13). Parque Nacional Mar\u00edtimo-Terrestre del Archipi\u00e9lago de Cabrera (Data Source). Available online: http:\/\/www.ideo-cabrera.ieo.es\/."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:44:12Z","timestamp":1760168652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,9]]},"references-count":65,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245007"],"URL":"https:\/\/doi.org\/10.3390\/rs13245007","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,9]]}}}