{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:46:05Z","timestamp":1782949565002,"version":"3.54.5"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031936968","type":"print"},{"value":"9783031936975","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"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":[[2026]]},"DOI":"10.1007\/978-3-031-93697-5_29","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T13:48:54Z","timestamp":1753278534000},"page":"407-422","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Super-Resolution Enhanced Tree Classification in\u00a0Satellite Images Using Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Nisha","family":"Shamsudin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V R","family":"Bindu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Ankita, Mittal, S.: Image classification of satellite using vgg16 model. In: 2024 2nd International Conference on Disruptive Technologies (ICDT), pp. 401\u2013404 (2024). https:\/\/doi.org\/10.1109\/ICDT61202.2024.10489685","DOI":"10.1109\/ICDT61202.2024.10489685"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C.: Signal and Image Processing for Remote Sensing. CRC Press (2024)","DOI":"10.1201\/9781003382010"},{"issue":"2","key":"29_CR3","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)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR4","doi-asserted-by":"publisher","unstructured":"Elaksher, A., Omar, I., Sanjenis, D., Velasco, J.R., Lao, M.: An automated system for 2d building detection from UAV-based geospatial datasets. Optics Lasers Eng. 184 (2025). https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108602","DOI":"10.1016\/j.optlaseng.2024.108602"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Fayaz, M., Dang, L.M., Moon, H.: Enhancing land cover classification via deep ensemble network. Knowl.-Based Syst. 305 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.112611","DOI":"10.1016\/j.knosys.2024.112611"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Fu, Y., Xie, D., Wang, Z., Yi, C., Guo, L., Wu, Y.: A super-resolution enhancement algorithm for remote sensing images using conditional controlled diffusion models. J. Geo-Inform. Sci. 26(10), 2384 \u2013 2393 (2024). https:\/\/doi.org\/10.12082\/dqxxkx.2024.240315","DOI":"10.12082\/dqxxkx.2024.240315"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.isprsjprs.2024.05.004","volume":"212","author":"J Guo","year":"2024","unstructured":"Guo, J., Hong, D., Liu, Z., Zhu, X.X.: Continent-wide urban tree canopy fine-scale mapping and coverage assessment in south America with high-resolution satellite images. ISPRS J. Photogramm. Remote. Sens. 212, 251\u2013273 (2024). https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.05.004","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"6","key":"29_CR8","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1049\/ipr2.12760","volume":"17","author":"J Guo","year":"2023","unstructured":"Guo, J., Lv, F., Shen, J., Liu, J., Wang, M.: An improved generative adversarial network for remote sensing image super-resolution. IET Image Proc. 17(6), 1852\u20131863 (2023). https:\/\/doi.org\/10.1049\/ipr2.12760","journal-title":"IET Image Proc."},{"key":"29_CR9","doi-asserted-by":"publisher","unstructured":"Hafsa, Belangour, Ouchra, A., Erraissi, A.: Machine learning algorithms for satellite image classification using google earth engine and landsat satellite data: Morocco case study. IEEE Access 11, 71127\u201371142 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3293828","DOI":"10.1109\/ACCESS.2023.3293828"},{"issue":"7","key":"29_CR10","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","volume":"12","author":"P Helber","year":"2019","unstructured":"Helber, P., Bischke, B., Dengel, A., Borth, D.: EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 12(7), 2217\u20132226 (2019). https:\/\/doi.org\/10.1109\/JSTARS.2019.2918242","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens."},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Hu, T., et al.: Global sparse attention network for remote sensing image super-resolution. Knowl.-Based Syst. 304 (2024). https:\/\/doi.org\/10.1016\/j.knosys.2024.112448","DOI":"10.1016\/j.knosys.2024.112448"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Illarionova, S., Shadrin, D., Shukhratov, I., Evteeva, K.e.a.: Benchmark for building segmentation on up-scaled sentinel-2 imagery. Remote Sensing (2023)","DOI":"10.3390\/rs15092347"},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Li, J.: Generative adversarial network for image super-resolution combining texture loss. Appl. Sci. 10(5) (2020). https:\/\/doi.org\/10.3390\/app10051729","DOI":"10.3390\/app10051729"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Karalasingham, S., Deo, R.C., Casillas-P\u00e9rez, D., Raj, N., Salcedo-Sanz, S.: Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery. Remote Sens. Appl. Society Environ. 36 (2024). https:\/\/doi.org\/10.1016\/j.rsase.2024.101333","DOI":"10.1016\/j.rsase.2024.101333"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Karwowska, K., Wierzbicki, D.: MCWESRGAN: Improving enhanced super-resolution generative adversarial network for satellite images. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 16 (2023). https:\/\/doi.org\/10.1109\/JSTARS.2023.3322642","DOI":"10.1109\/JSTARS.2023.3322642"},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Kim, B., An, E.J., Kim, S., Sri\u00a0Preethaa, K., Lee, D.E., Lukacs, R.: SRGAN-enhanced unsafe operation detection and classification of heavy construction machinery using cascade learning. Artif. Intell. Rev. 57(8) (2024). https:\/\/doi.org\/10.1007\/s10462-024-10839-7","DOI":"10.1007\/s10462-024-10839-7"},{"key":"29_CR17","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","volume":"152","author":"L Ma","year":"2019","unstructured":"Ma, L., et al.: Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J. Photogramm. Remote. Sens. 152, 166\u2013177 (2019)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Maiseli, B., Abdalla, A.T.: Seven decades of image super-resolution: achievements, challenges, and opportunities. Eurasip J. Adv. Signal Process. 2024(1) (2024). https:\/\/doi.org\/10.1186\/s13634-024-01170-y","DOI":"10.1186\/s13634-024-01170-y"},{"key":"29_CR20","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1109\/JSTARS.2023.3328997","volume":"17","author":"A Malczewska","year":"2024","unstructured":"Malczewska, A., Wielgosz, M.: How does super-resolution for satellite imagery affect different types of land cover? sentinel-2 case. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 17, 340\u2013363 (2024). https:\/\/doi.org\/10.1109\/JSTARS.2023.3328997","journal-title":"IEEE J. Selected Topics Appl. Earth Observ. Remote Sens."},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3336680","volume":"21","author":"J Min","year":"2024","unstructured":"Min, J., Lee, Y., Kim, D., Yoo, J.: Bridging the domain gap: A simple domain matching method for reference-based image super-resolution in remote sensing. IEEE Geosci. Remote Sens. Lett. 21, 1\u20135 (2024). https:\/\/doi.org\/10.1109\/LGRS.2023.3336680","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"29_CR22","doi-asserted-by":"publisher","unstructured":"Ouchra, H., Belangour, A., Erraissi, A.: Machine learning for satellite image classification: A comprehensive review. In: 2022 International Conference on Data Analytics for Business and Industry (ICDABI), pp.\u00a01\u20135 (2022). https:\/\/doi.org\/10.1109\/ICDABI56818.2022.10041606","DOI":"10.1109\/ICDABI56818.2022.10041606"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Pan, Y., Tang, J., Tjahjadi, T.: Lpsrgan: Generative adversarial networks for super-resolution of license plate image. Neurocomputing 580 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2024.127426","DOI":"10.1016\/j.neucom.2024.127426"},{"key":"29_CR24","doi-asserted-by":"publisher","unstructured":"Pandey, B., Pandey, M.S.: Enhanced satellite image classification using deep convolutional neural network. In: 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), pp.\u00a01\u20136 (2024). https:\/\/doi.org\/10.1109\/IC3SE62002.2024.10593555","DOI":"10.1109\/IC3SE62002.2024.10593555"},{"key":"29_CR25","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1109\/ACCESS.2023.3349023","volume":"12","author":"H Park","year":"2024","unstructured":"Park, H.: Semantic super-resolution via self-distillation and adversarial learning. IEEE Access 12, 2361\u20132370 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2023.3349023","journal-title":"IEEE Access"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"29_CR27","series-title":"Lecture Notes in Networks and Systems","volume-title":"Soft Computing for Problem Solving","author":"P Singh","year":"2023","unstructured":"Singh, P., Garg, R., Prasad, S.: Tree detection from urban developed areas in high-resolution satellite images. In: Thakur, M., Agnihotri, S., Rajpurohit, B., Pant, M., Deep, K., Nagar, A. (eds.) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol. 547. Springer, Singapore (2023)"},{"key":"29_CR28","doi-asserted-by":"publisher","unstructured":"Ulla, S., et al.: SatNet: a lightweight satellite image classification model using deep convolutional neural network. In: 2023 IEEE International Conference on Telecommunications and Photonics (ICTP), pp. 01\u201305 (2023). https:\/\/doi.org\/10.1109\/ICTP60248.2023.10490785","DOI":"10.1109\/ICTP60248.2023.10490785"},{"key":"29_CR29","doi-asserted-by":"publisher","unstructured":"Vinod, P., Behera, M., Jaya\u00a0Prakash, A., Hebbar, R., Srivastav, S.: A novel multitask transformer deep learning architecture for joint classification and segmentation of horticulture plantations using very high-resolution satellite imagery. Comput. Electron. Agricult.227 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109540","DOI":"10.1016\/j.compag.2024.109540"},{"key":"29_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3258965","volume":"20","author":"C Wang","year":"2023","unstructured":"Wang, C., Zhang, X., Yang, W., Li, X., Lu, B., Wang, J.: MSAGAN: A new super-resolution algorithm for multispectral remote sensing image based on a multiscale attention gan network. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/LGRS.2023.3258965","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"29_CR31","unstructured":"Wang, X., et al.: Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp.\u00a00\u20130 (2018)"},{"key":"29_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3248069","volume":"20","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Shao, Z., Lu, T., Wu, C., Wang, J.: Remote sensing image super-resolution via multiscale enhancement network. IEEE Geosci. Remote Sens. Lett. 20, 1\u20135 (2023). https:\/\/doi.org\/10.1109\/LGRS.2023.3248069","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"29_CR33","doi-asserted-by":"publisher","unstructured":"Yin, Z., et al.: Super-resolution water body mapping with a feature collaborative CNN model by fusing sentinel-1 and sentinel-2 images. Int. J. Appl. Earth Observ. Geoinform. 134 (2024). https:\/\/doi.org\/10.1016\/j.jag.2024.104176","DOI":"10.1016\/j.jag.2024.104176"},{"key":"29_CR34","doi-asserted-by":"publisher","unstructured":"Yu, S., Wu, K., Zhang, G., Yan, W., Wang, X., Tao, C.: MEFSAR-GAN: A multi-exposure feedback and super-resolution multitask network via generative adversarial networks. Remote Sensing 16(18) (2024). https:\/\/doi.org\/10.3390\/rs16183501","DOI":"10.3390\/rs16183501"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-93697-5_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:26:11Z","timestamp":1782948371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-93697-5_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"ISBN":["9783031936968","9783031936975"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-93697-5_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"20 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2024.iiitdm.ac.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}