{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T11:31:50Z","timestamp":1743161510932,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819619030"},{"type":"electronic","value":"9789819619047"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-1904-7_8","type":"book-chapter","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T21:23:33Z","timestamp":1740345813000},"page":"83-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Super-Resolution of Satellite Images Using Landsat Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8001-9454","authenticated-orcid":false,"given":"Ivan","family":"Sharshov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-5896","authenticated-orcid":false,"given":"Vladimir","family":"Berezovsky","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6716-4419","authenticated-orcid":false,"given":"Kseniya","family":"Shoshina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-8937","authenticated-orcid":false,"given":"Roman","family":"Aleshko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6322-6371","authenticated-orcid":false,"given":"Irina","family":"Vasendina","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"issue":"6","key":"8_CR1","doi-asserted-by":"publisher","first-page":"3512","DOI":"10.1109\/TGRS.2018.2885506","volume":"57","author":"W Ma","year":"2019","unstructured":"Ma, W., Pan, Z., Guo, J., Lei, B.: Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net. IEEE Trans. Geosci. Remote Sens. 57(6), 3512\u20133527 (2019). https:\/\/doi.org\/10.1109\/TGRS.2018.2885506","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"8_CR2","doi-asserted-by":"publisher","first-page":"6792","DOI":"10.1109\/TGRS.2018.2843525","volume":"56","author":"JM Haut","year":"2018","unstructured":"Haut, J.M., Fernandez-Beltran, R., Paoletti, M.E., Plaza, J., Plaza, A., Pla, F.: A new deep generative network for unsupervised remote sensing single-image super-resolution. IEEE Trans. Geosci. Remote Sens. 56(11), 6792\u20136810 (2018). https:\/\/doi.org\/10.1109\/TGRS.2018.2843525","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Karwowska, K., Wierzbicki, D.: Using super-resolution algorithms for small satellite imagery: a systematic review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 15, 3292\u20133312 (2022). https:\/\/doi.org\/10.1109\/JSTARS.2022.3167646","DOI":"10.1109\/JSTARS.2022.3167646"},{"key":"8_CR4","doi-asserted-by":"publisher","unstructured":"Ren, Z., He, L., Lu, J. :Context aware edge-enhanced GAN for remote sensing image super-resolution. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 1\u201315 (2023). https:\/\/doi.org\/10.1109\/JSTARS.2023.3333271","DOI":"10.1109\/JSTARS.2023.3333271"},{"issue":"2","key":"8_CR5","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Kim, J., Lee, J.K. , Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016). https:\/\/doi.org\/10.48550\/arXiv.1511.04587","DOI":"10.48550\/arXiv.1511.04587"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR, vol. abs\/1511.06434 (2016). https:\/\/doi.org\/10.48550\/arXiv.1511.06434","DOI":"10.48550\/arXiv.1511.06434"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105\u2013114 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.19","DOI":"10.1109\/CVPR.2017.19"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.298","DOI":"10.1109\/CVPR.2017.298"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks, vol. 11133 LNCS, pp. 63\u201379 (2019)","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.3390\/rs11131588","volume":"11","author":"T Lu","year":"2019","unstructured":"Lu, T., Wang, J., Zhang, Y., Wang, Z., Jiang, J.: Satellite image super-resolution via multi-scale residual deep neural network. Remote Sens. 11, 1588 (2019)","journal-title":"Remote Sens."},{"issue":"10","key":"8_CR12","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1109\/83.791975","volume":"8","author":"B Kamgar-Parsi","year":"1999","unstructured":"Kamgar-Parsi, B., Kamgar-Parsi, B., Rosenfeld, A.: Optimally isotropic Laplacian operator. IEEE Trans. Image Process. 8(10), 1467\u20131472 (1999). https:\/\/doi.org\/10.1109\/83.791975","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.3390\/rs12091432","volume":"12","author":"J Rabbi","year":"2020","unstructured":"Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., Chao, D.: Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network. Remote Sens. 12, 1432 (2020). https:\/\/doi.org\/10.3390\/rs12091432","journal-title":"Remote Sens."},{"key":"8_CR14","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","author":"K Simonyan","year":"2015","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR (2015). https:\/\/doi.org\/10.48550\/arXiv.1409.1556","journal-title":"CoRR"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"USGS EROS Archive - Aerial Photography - High Resolution Orthoimagery (HRO), USGS. https:\/\/doi.org\/10.5066\/F73X84W6","DOI":"10.5066\/F73X84W6"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Collection 2 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-2 Science Product Digital Object Identifier (DOI) number. https:\/\/doi.org\/10.5066\/P9C7I13B","DOI":"10.5066\/P9C7I13B"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Collection 2 Landsat 8\u20139 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) Level-2 Science Product Digital Object Identifier (DOI) number. https:\/\/doi.org\/10.5066\/P9OGBGM6","DOI":"10.5066\/P9OGBGM6"},{"key":"8_CR18","unstructured":"Sayler, K.: Landsat 8\u20139 Collection 2 (C2) Level 2 Science Product (L2SP) Guide. https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/media\/files\/LSDS-1619_Landsat8-9-Collection2-Level2-Science-Product-Guide-v6.pdf. Accessed 3 April 2024"},{"key":"8_CR19","unstructured":"Sayler, K., Zanter, K.: Landsat 4\u20137 Collection 2 (C2) Level 2 Science Product (L2SP) Guide. Department of the Interior U.S. Geological Survey. https:\/\/d9-wret.s3.us-west-2.amazonaws.com\/assets\/palladium\/production\/s3fs-public\/media\/files\/LSDS-1618_Landsat-4-7_C2-L2-ScienceProductGuide-v4.pdf. Accessed 3 April 2024"},{"key":"8_CR20","unstructured":"EPSG:9001. https:\/\/epsg.io\/9001. Accessed 3 April 2024"},{"key":"8_CR21","unstructured":"Snow, A.: pyproj. Python interface to PROJ (cartographic projections and coordinate transformations library). https:\/\/github.com\/pyproj4\/pyproj. Accessed 3 April 2024"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Presented at the the 3rd International Conference for Learning Representations, San Diego (2017). https:\/\/doi.org\/10.48550\/arXiv.1412.6980","DOI":"10.48550\/arXiv.1412.6980"},{"key":"8_CR23","unstructured":"Fomin, V., Anmol, J., Desroziers, S., Kriss, J., Tejani, A.: High-level library to help with training neural networks in PyTorch. https:\/\/github.com\/pytorch\/ignite. Accessed 3 April 2024"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1904-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T21:23:37Z","timestamp":1740345817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1904-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619030","9789819619047"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1904-7_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}