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The proposed architectures, including sparse and variational autoencoder counterparts, form a comprehensive image compression framework with quantization, and various entropy coders are applied to the EuroSat dataset (RGB and multispectral). The experimental results show the models\u2019 superiority over the JPEG family and recent state-of-the-art methods, achieving up to 3.3, 1.4, and 0.6% improvements in the peak signal-to-noise ratio, structural similarity index, and multiscale structural similarity index, respectively. Moreover, performance analysis in terms of computational complexity, processing time, and memory usage highlights the efficiency of the proposed models. A case study conducted on a real scene from the Sentinel-2 satellite further validates the compatibility of the proposed models with modern artificial intelligence chipsets. <\/jats:p>","DOI":"10.2514\/1.i011445","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T14:52:19Z","timestamp":1734447139000},"page":"336-357","update-policy":"https:\/\/doi.org\/10.2514\/aiaa_crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid Spatial\u2013Spectral Autoencoder Models for Lossy Satellite Image Compression"],"prefix":"10.2514","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0075-6715","authenticated-orcid":false,"given":"Mohamed Ahmed","family":"Badr","sequence":"first","affiliation":[{"name":"Military Technical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed Fathy","family":"Elrewainy","sequence":"additional","affiliation":[{"name":"Military Technical College"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Abdelmoneim Taha","family":"Elshafey","sequence":"additional","affiliation":[{"name":"Military Technical College"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1387","reference":[{"key":"r2","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2008.12.006"},{"key":"r3","doi-asserted-by":"publisher","DOI":"10.3390\/rs12101590"},{"key":"r4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2794179"},{"key":"r5","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012119792-6\/50107-8"},{"key":"r6","volume":"10","author":"Ma X.","year":"2023","journal-title":"Displays"},{"key":"r7","first-page":"44","volume-title":"Neurocomputing","volume":"300","author":"Hussain A. 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