{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:07:17Z","timestamp":1743084437045,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031274398"},{"type":"electronic","value":"9783031274404"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-27440-4_15","type":"book-chapter","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:39:41Z","timestamp":1685479181000},"page":"152-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Virtual Reconstruction of\u00a0Adaptive Spectral and\u00a0Spatial Features Based on\u00a0CNN for\u00a0HSI Classification"],"prefix":"10.1007","author":[{"given":"Maissa","family":"Hamouda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MedSalim","family":"bouhlel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Khader, A., Yang, J., Xiao, L.: NMF-DuNet: Nonnegative matrix factorization inspired deep unrolling networks for hyperspectral and multispectral image fusion. IEEE J. Selected Topics Appl. Earth Observations Remote Sensing, pp. 1\u201317 (2022)","DOI":"10.1109\/JSTARS.2022.3189551"},{"issue":"04","key":"15_CR2","doi-asserted-by":"publisher","first-page":"1950019","DOI":"10.1142\/S0219467819500190","volume":"19","author":"M Hamouda","year":"2019","unstructured":"Hamouda, M., Ettabaa, K.S., Bouhlel, M.S.: Framework for automatic selection of kernels based on convolutional neural networks and ckmeans clustering algorithm. Int. J. Image Graph. 19(04), 1950019 (2019)","journal-title":"Int. J. Image Graph."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Wang, L.,\u00a0Wang, L., Wang, H., Wang, X., Bruzzone, L.: \u201cSPCNet: A subpixel convolution-based change detection network for hyperspectral images with different spatial resolutions. IEEE Trans. Geosci. Remote Sensing 60 1\u20131 (2022)","DOI":"10.1109\/TGRS.2022.3189188"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Fu, H., et al.: A novel band selection and spatial noise reduction method for hyperspectral image classification. IEEE Trans. Geosci. Remote Sensing 60, 1\u201313 (2022)","DOI":"10.1109\/TGRS.2022.3189015"},{"issue":"10","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1049\/iet-ipr.2019.1282","volume":"14","author":"M Hamouda","year":"2020","unstructured":"Hamouda, M., Ettabaa, K.S., Bouhlel, M.S.: Smart feature extraction and classification of hyperspectral images based on convolutional neural networks. IET Image Proc. 14(10), 1999\u20132005 (2020)","journal-title":"IET Image Proc."},{"issue":"38","key":"15_CR6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.52547\/jist.16105.10.38.111","volume":"10","author":"M Hasheminejad","year":"2022","unstructured":"Hasheminejad, M.: Optimized kernel nonparametric weighted feature extraction for hyperspectral image classification. J. Inform. Syst. Telecommun. (JIST) 10(38), 111\u2013119 (2022)","journal-title":"J. Inform. Syst. Telecommun. (JIST)"},{"issue":"9","key":"15_CR7","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.3390\/rs14092227","volume":"14","author":"L Zhou","year":"2022","unstructured":"Zhou, L., Xu, E., Hao, S., Ye, Y., Zhao, K.: Data-wise spatial regional consistency re-enhancement for hyperspectral image classification. Remote Sensing 14(9), 2227 (2022)","journal-title":"Remote Sensing"},{"issue":"5","key":"15_CR8","doi-asserted-by":"publisher","first-page":"702","DOI":"10.3390\/land11050702","volume":"11","author":"R Yang","year":"2022","unstructured":"Yang, R., Zhou, Q., Fan, B., Wang, Y.: Land cover classification from hyperspectral images via local nearest neighbor collaborative representation with tikhonov regularization. Land 11(5), 702 (2022)","journal-title":"Land"},{"key":"15_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1007\/978-3-319-94211-7_34","volume-title":"Image and Signal Processing","author":"M Hamouda","year":"2018","unstructured":"Hamouda, M., Saheb Ettabaa, K., Bouhlel, M.S.: Adaptive batch extraction for hyperspectral image classification based on convolutional neural network. In: Mansouri, A., El Moataz, A., Nouboud, F., Mammass, D. (eds.) ICISP 2018. LNCS, vol. 10884, pp. 310\u2013318. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-94211-7_34"},{"key":"15_CR10","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/978-3-030-96308-8_53","volume-title":"Intelligent Systems Design and Applications","author":"M Hamouda","year":"2022","unstructured":"Hamouda, M., Bouhlel, M.S.: Hybrid neural network for hyperspectral satellite image classification (HNN). In: Abraham, A., Gandhi, N., Hanne, T., Hong, T.-P., Nogueira Rios, T., Ding, W. (eds.) ISDA 2021. LNNS, vol. 418, pp. 567\u2013575. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-96308-8_53"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, F., Dong, J., Du, Q.: Adaptive DropBlock-enhanced generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sensing 59(6), 5040\u20135053 (2021)","DOI":"10.1109\/TGRS.2020.3015843"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Hamouda, M., Ettabaa, K.S., Bouhlel, M.S.: Modified convolutional neural network based on adaptive patch extraction for hyperspectral image classification. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018, pp. 1\u20137 (2018)","DOI":"10.1109\/FUZZ-IEEE.2018.8491647"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, Y., Zhang, L., Liu, M., Plaza, A.: DRFL-VAT: Deep representative feature learning with virtual adversarial training for semi-supervised classification of hyperspectral image. IEEE Trans. Geosc Remote Sensing. 60 1\u20131 (2022)","DOI":"10.1109\/TGRS.2022.3187187"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Sun, K., Wang, A., Sun, X., Zhang, T.: Hyperspectral image classification method based on m-3dcnn-attention. J. Appl. Remote Sensing 16(02) (2022)","DOI":"10.1117\/1.JRS.16.026507"},{"key":"15_CR15","unstructured":"Ranasinghe, K.: Gauss: Guided encoder-decoder architecture for hyperspectral unmixing with spatial smoothness (2022)"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Palop, J.J., Mucke, L., Roberson, E.D.: Quantifying biomarkers of cognitive dysfunction and neuronal network hyperexcitability in mouse models of alzheimer\u2019s disease: depletion of calcium-dependent proteins and inhibitory hippocampal remodeling. in Alzheimer\u2019s Disease and Frontotemporal Dementia. Springer, 2010, pp. 245\u2013262. https:\/\/doi.org\/10.1007\/978-1-60761-744-0_17","DOI":"10.1007\/978-1-60761-744-0_17"},{"key":"15_CR17","unstructured":"Lin, K., et al.: Outdoor detection of the pollution degree of insulating materials based on hyperspectral model transfer. Available at SSRN 4157180"},{"key":"15_CR18","unstructured":"GIC.: Hyperspectral remote sensing scenes. Grupo de Inteligencia Computacional (2014)"},{"key":"15_CR19","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/978-3-030-63820-7_42","volume-title":"Neural Information Processing","author":"M Hamouda","year":"2020","unstructured":"Hamouda, M., Bouhlel, M.S.: Dual convolutional neural networks for hyperspectral satellite images classification (DCNN-HSI). In: Yang, H., et al. (eds.) ICONIP 2020. CCIS, vol. 1332, pp. 369\u2013376. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-63820-7_42"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27440-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:44:38Z","timestamp":1685479478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27440-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031274398","9783031274404"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27440-4_15","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}