{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T09:56:32Z","timestamp":1747734992074},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s12145-023-00948-2","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T09:05:12Z","timestamp":1675155912000},"page":"877-886","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multitemporal hyperspectral satellite image analysis and classification using fast scale invariant feature transform and deep learning neural network classifier"],"prefix":"10.1007","volume":"16","author":[{"given":"G.","family":"Vinuja","sequence":"first","affiliation":[]},{"given":"N. Bharatha","family":"Devi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"issue":"3","key":"948_CR1","doi-asserted-by":"publisher","first-page":"771","DOI":"10.32604\/iasc.2021.018039","volume":"30","author":"M Alyas Khan","year":"2021","unstructured":"Alyas Khan M, Ali M, Shah M et al (2021) Machine learning-based detection and classification of walnut fungi diseases. Intell Autom Soft Comput 30(3):771\u2013785","journal-title":"Intell Autom Soft Comput"},{"issue":"3","key":"948_CR2","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/systems10030052","volume":"10","author":"D Cabrera","year":"2022","unstructured":"Cabrera D, Cabrera L, Cabrera E (2022) Perspectives organize information in mind and nature: empirical findings of point-view perspective (p) in cognitive and material complexity. Systems 10(3):52","journal-title":"Systems"},{"issue":"1","key":"948_CR3","first-page":"9","volume":"2","author":"G De Luca","year":"2022","unstructured":"De Luca G (2022) A survey of nisq era hybrid quantum classical machine learning research. J Artif Intell Technol 2(1):9\u201315","journal-title":"J Artif Intell Technol"},{"key":"948_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.isprsjprs.2019.09.008","volume":"158","author":"D Hong","year":"2019","unstructured":"Hong D, Yokoya N, Chanussot J, Xu J, Xiao Xiang Zhu (2019) Learning to propagate labels on graphs: an iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction. ISPRS J photogrammetry remote Sens 158:35\u201349","journal-title":"ISPRS J photogrammetry remote Sens"},{"issue":"7","key":"948_CR5","doi-asserted-by":"publisher","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","volume":"59","author":"D Hong","year":"2020","unstructured":"Hong D, Gao L, Yao J, Zhang B, Plaza A, Jocelyn Chanussot (2020) Graph convolutional networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 59(7):5966\u20135978","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"948_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3172371","volume":"60","author":"D Hong","year":"2021","unstructured":"Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Jocelyn Chanussot (2021) SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"948_CR7","doi-asserted-by":"crossref","unstructured":"Jacobsen R, Bernabel CA, Hobbs M, Oishi N, Puig-Hall M, Shannon Z (2022) Machine learning: paving the way for more efficient disaster relief, AIAA SCITECH 2022Forum, p 0397","DOI":"10.2514\/6.2022-0397"},{"issue":"1","key":"948_CR8","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1137\/21M1395351","volume":"4","author":"J Jia","year":"2022","unstructured":"Jia J, Benson AR (2022) A unifying generative model for graph learning algorithms: label propagation, graph convolutions, and combinations. SIAM J Math Data Sci 4(1):100\u2013125","journal-title":"SIAM J Math Data Sci"},{"issue":"7","key":"948_CR9","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1007\/s10994-021-05972-1","volume":"111","author":"J-F Karasiak, Nicolas","year":"2022","unstructured":"Karasiak, Nicolas J-F, Dejoux CM, Sheeren D (2022) Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing. Mach Learn 111(7):2715\u20132740","journal-title":"Mach Learn"},{"issue":"4","key":"948_CR10","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1007\/s11431-021-1978-6","volume":"65","author":"TianZhu Liu","year":"2022","unstructured":"Liu T, Gu Y Jia X (2022) Class-guided coupled dictionary learning for multispectral-hyperspectral remote sensing image collaborative classification. Sci China Technological Sci 65(4):744\u2013758","journal-title":"Sci China Technological Sci"},{"issue":"6","key":"948_CR11","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.3390\/rs14061453","volume":"14","author":"V Nasiri","year":"2022","unstructured":"Nasiri V, Darvishsefat AA, Arefi H, Griess VC, Sadeghi SMM, Borz SA (2022) Modeling forest canopy cover: a synergistic use of sentinel 2, aerial photogrammetry data, and machine learning. Remote Sens 14(6):1453","journal-title":"Remote Sens"},{"key":"948_CR12","doi-asserted-by":"crossref","unstructured":"Sun Y, Liu B, Yu X, Yu A, Zhang P, Xue Z (2022) Exploiting discriminative advantage of Spectrum for Hyperspectral Image classification: SpectralFormer enhanced by Spectrum Motion feature. IEEE Geoscience and Remote Sensing Letters","DOI":"10.1109\/LGRS.2022.3228531"},{"issue":"20","key":"948_CR13","doi-asserted-by":"publisher","first-page":"5891","DOI":"10.1080\/10106049.2021.1926552","volume":"37","author":"K Thamaga","year":"2022","unstructured":"Thamaga K, Humphrey T, Dube, Shoko C (2022) Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa.\u00a0 Geocarto International 37(20):5891\u20135913","journal-title":"\u201d Geocarto International"},{"key":"948_CR14","first-page":"6316","volume":"34","author":"S Wan","year":"2021","unstructured":"Wan S, Zhan Y, Liu L, Yu B, Pan S, Chen Gong (2021) Contrastive graph poisson networks: semi-supervised learning with extremely limited labels. Adv Neural Inf Process Syst 34:6316\u20136327","journal-title":"Adv Neural Inf Process Syst"},{"key":"948_CR15","doi-asserted-by":"crossref","unstructured":"Wang P, Bayram B, Sertel E (2022) A comprehensive review on deep learning based remote sensing image super-resolution methods. Earth-Science Reviews 104110","DOI":"10.1016\/j.earscirev.2022.104110"},{"key":"948_CR16","doi-asserted-by":"crossref","unstructured":"Zheng Z, Du S, Taubenb\u00f6ck H, Zhang X (2022) Remote sensing techniques in the investigation of aeolian sand dunes: a review of recent advances. Remote Sens Environ 271, Article ID 112913","DOI":"10.1016\/j.rse.2022.112913"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-00948-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-00948-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-00948-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T03:40:22Z","timestamp":1677037222000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-00948-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["948"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-00948-2","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,31]]},"assertion":[{"value":"17 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors mentioned in the manuscript have agreed for authorship, read and approved the manuscript, and given consent for submission and subsequent publication of the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable for this section.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable for this section.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no conflict of interest in this manuscript regarding publication.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}