{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:35:28Z","timestamp":1769823328011,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Military University of Technology, Faculty of Civil Engineering and Geodesy","award":["UGB\/22-786\/2022\/WAT"],"award-info":[{"award-number":["UGB\/22-786\/2022\/WAT"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dynamic technological progress has contributed to the development of systems imaging of the Earth\u2019s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolution image (LR) and an algorithm using deep neural networks. The limitation of these solutions is the input size parameter, which defines the image size that is adopted by a given neural network. Unfortunately, the value of this parameter is often much smaller than the size of the images obtained by Earth Observation satellites. In this article, we presented a new methodology for improving the resolution of an entire satellite image, using a window function. In addition, we conducted research to improve the resolution of satellite images acquired with the World View 2 satellite using the ESRGAN network, we determined the number of buffer pixels that will make it possible to obtain the best image quality. The best reconstruction of the entire satellite imagery using generative neural networks was obtained using a Triangular window (for 10% coverage). The Hann-Poisson window worked best when more overlap between images was used.<\/jats:p>","DOI":"10.3390\/rs14246285","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions"],"prefix":"10.3390","volume":"14","author":[{"given":"Kinga","family":"Karwowska","sequence":"first","affiliation":[{"name":"Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6192-3894","authenticated-orcid":false,"given":"Damian","family":"Wierzbicki","sequence":"additional","affiliation":[{"name":"Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"1","article-title":"DNN-Based Peak Sequence Classification CFAR Detection Algorithm for High-Resolution FMCW Radar","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"1","article-title":"Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion","volume":"60","author":"Cui","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MAES.2021.3117369","article-title":"A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances","volume":"37","author":"Oveis","year":"2022","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7570","DOI":"10.1109\/TGRS.2020.2981082","article-title":"River Ice Segmentation With Deep Learning","volume":"58","author":"Singh","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8780","DOI":"10.1109\/TGRS.2020.2990640","article-title":"Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images","volume":"58","author":"Saha","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4590","DOI":"10.1109\/TGRS.2020.2964288","article-title":"Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network","volume":"58","author":"Mei","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TGRS.2014.2335818","article-title":"Improving the Spatial Resolution of Landsat TM\/ETM+ Through Fusion With SPOT5 Images via Learning-Based Super-Resolution","volume":"53","author":"Song","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1007\/s11629-021-7254-9","article-title":"Literature Review and Bibliometric Analysis on Data-Driven Assessment of Landslide Susceptibility","volume":"19","author":"Lima","year":"2022","journal-title":"J. Mt. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xia, D., Tang, H., Sun, S., Tang, C., and Zhang, B. (2022). Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification. Remote Sens., 14.","DOI":"10.3390\/rs14112707"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4524","DOI":"10.1109\/TGRS.2016.2543660","article-title":"Sea Ice Concentration Estimation During Melt From Dual-Pol SAR Scenes Using Deep Convolutional Neural Networks: A Case Study","volume":"54","author":"Wang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, L., Scott, K.A., and Clausi, D.A. (2017). Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9050408"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1109\/TGRS.2019.2892723","article-title":"Estimating Sea Ice Concentration From SAR: Training Convolutional Neural Networks With Passive Microwave Data","volume":"57","author":"Cooke","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Scarpa, G., Gargiulo, M., Mazza, A., and Gaetano, R. (2018). A CNN-Based Fusion Method for Feature Extraction from Sentinel Data. Remote Sens., 10.","DOI":"10.3390\/rs10020236"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Oveis, A.H., Giusti, E., Ghio, S., and Martorella, M. (2021, January 7\u201314). CNN for Radial Velocity and Range Components Estimation of Ground Moving Targets in SAR. Proceedings of the 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA.","DOI":"10.1109\/RadarConf2147009.2021.9455155"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Lu, C., and Jiang, W. (2018). Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression. Sensors, 18.","DOI":"10.3390\/s18092851"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"250","DOI":"10.3176\/proc.2014.2S.06","article-title":"Imaging System for Nanosatellite Proximity Operations","volume":"63","author":"Kuuste","year":"2014","journal-title":"Proc. Est. Acad. Sci."},{"key":"ref_19","unstructured":"Blommaert, J., Delaur\u00e9, B., Livens, S., Nuyts, D., Moreau, V., Callut, E., Habay, G., Vanhoof, K., Caubo, M., and Vandenbussche, J. (2022, October 18). CHIEM: A New Compact Camera for Hyperspectral Imaging. Available online: https:\/\/www.researchgate.net\/publication\/321214165_CHIEM_A_new_compact_camera_for_hyperspectral_imaging."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2018, January 18\u201322). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/JSTARS.2022.3167646","article-title":"Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review","volume":"15","author":"Karwowska","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lu, T., Wang, J., Zhang, Y., Wang, Z., and Jiang, J. (2019). Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11131588"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TAP.1986.1143830","article-title":"Multiple Emitter Location and Signal Parameter Estimation","volume":"34","author":"Schmidt","year":"1986","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_25","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","article-title":"Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning","volume":"53","author":"Han","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6270","DOI":"10.1109\/JSTARS.2021.3089519","article-title":"Prior-Information Auxiliary Module: An Injector to a Deep Learning Bridge Detection Model","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3040273","article-title":"A New Spatial-Oriented Object Detection Framework for Remote Sensing Images","volume":"60","author":"Yu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"6214","article-title":"Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery","volume":"56","author":"Kemker","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6352","DOI":"10.1109\/JSTARS.2020.3031020","article-title":"Transfer Learning With CNNs for Segmentation of PALSAR-2 Power Decomposition Components","volume":"13","author":"Vinayaraj","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10357","DOI":"10.1109\/JSTARS.2021.3116094","article-title":"Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models","volume":"14","author":"Antropov","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","first-page":"1","article-title":"Continual Learning with Structured Inheritance for Semantic Segmentation in Aerial Imagery","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zuo, Z., and Li, Y. (2021, January 17\u201322). A SAR-to-Optical Image Translation Method Based on PIX2PIX. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS47720.2021.9555111"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TGRS.2020.3021819","article-title":"SMAPGAN: Generative Adversarial Network-Based Semisupervised Styled Map Tile Generation Method","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6054","DOI":"10.1109\/TGRS.2017.2719738","article-title":"Learning Aerial Image Segmentation from Online Maps","volume":"55","author":"Kaiser","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","first-page":"1","article-title":"Translation of Aerial Image Into Digital Map via Discriminative Segmentation and Creative Generation","volume":"60","author":"Fu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3088686","article-title":"Spectral Synthesis for Geostationary Satellite-to-Satellite Translation","volume":"60","author":"Vandal","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","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). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C.C., Qiao, Y., and Tang, X. (2018). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. arXiv.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/LGRS.2019.2934493","article-title":"S3: A Spectral-Spatial Structure Loss for Pan-Sharpening Networks","volume":"17","author":"Choi","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/LGRS.2019.2930308","article-title":"Vehicle Detection in Remote Sensing Images Leveraging on Simultaneous Super-Resolution","volume":"17","author":"Ji","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/JSTARS.2020.3037225","article-title":"SRARNet: A Unified Framework for Joint Superresolution and Aircraft Recognition","volume":"14","author":"Tang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shen, C., Ji, X., and Miao, C. (2019, January 4\u20139). Real-Time Image Stitching with Convolutional Neural Networks. Proceedings of the 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), Irkutsk, Russia.","DOI":"10.1109\/RCAR47638.2019.9044010"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, X., He, L., and Li, X. (2021, January 9\u201312). Image Stitching via Convolutional Neural Network. Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC54389.2021.9674411"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108534","DOI":"10.1016\/j.sigpro.2022.108534","article-title":"Image Stitching by Disparity-Guided Multi-Plane Alignment","volume":"197","author":"Lin","year":"2022","journal-title":"Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pielawski, N., and W\u00e4hlby, C. (2020). Introducing Hann Windows for Reducing Edge-Effects in Patch-Based Image Segmentation. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229839"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Keelan, B. (2002). Handbook of Image Quality: Characterization and Prediction, CRC Press.","DOI":"10.1201\/9780203910825"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A Universal Image Quality Index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_52","unstructured":"Goetz, A., Boardman, W., and Yunas, R. (1992). Discrimination among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm. JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop, AVIRIS Workshop."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image Information and Visual Quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Prabhu, K.M.M. (2017). Window Functions and Their Applications in Signal Processing, CRC Press.","DOI":"10.1201\/9781315216386"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, Y., Gao, Y., and Yue, S. (2016, January 22\u201323). Using Guided Filtering to Improve Gram-Schmidt Based Pansharpening Method for GeoEye-1 Satellite Images. Proceedings of the 4th International Conference on Information Systems and Computing Technology, Shanghai, China.","DOI":"10.2991\/isct-16.2016.6"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sekrecka, A., and Kedzierski, M. (2018). Integration of Satellite Data with High Resolution Ratio: Improvement of Spectral Quality with Preserving Spatial Details. Sensors, 18.","DOI":"10.3390\/s18124418"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/TGRS.2020.2994253","article-title":"Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks","volume":"59","author":"Dong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/JSTARS.2020.3040176","article-title":"SANet: A Sea\u2013Land Segmentation Network Via Adaptive Multiscale Feature Learning","volume":"14","author":"Cui","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6285\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:39:38Z","timestamp":1760146778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6285"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":58,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246285"],"URL":"https:\/\/doi.org\/10.3390\/rs14246285","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]}}}