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These are due to the limited computing capacity available in remote sensing platforms and the high computational cost of compression algorithms for hyperspectral data, especially when the number of bands is large. To address these issues, a channel clusterisation strategy is proposed, which reduces the computational demands of learned compression methods for real scenarios and is scalable for different sources of data with varying numbers of bands. The proposed method is compatible with an embedded implementation for state-of-the-art on board hardware, a first for a ML hyperspectral data compression method. In terms of coding performance, our proposal surpasses established lossy methods such as JPEG 2000 preceded by a spectral Karhunen-Lo\u00e8ve Transform (KLT), in clusters of 3 to 7 bands, achieving a PSNR improvement of, on average, 9 dB for AVIRIS and 3 dB for Hyperion images.<\/jats:p>","DOI":"10.3390\/rs15184422","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"4422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Scalable Reduced-Complexity Compression of Hyperspectral Remote Sensing Images Using Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1038-6413","authenticated-orcid":false,"given":"Sebasti\u00e0","family":"Mijares i Verd\u00fa","sequence":"first","affiliation":[{"name":"Department of Information and Communications Engineering, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0769-8985","authenticated-orcid":false,"given":"Johannes","family":"Ball\u00e9","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, CA 94043, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7531-9890","authenticated-orcid":false,"given":"Valero","family":"Laparra","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, Universitat de Val\u00e8ncia, 46980 Paterna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1551-3680","authenticated-orcid":false,"given":"Joan","family":"Bartrina-Rapesta","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-4337","authenticated-orcid":false,"given":"Miguel","family":"Hern\u00e1ndez-Cabronero","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4729-9292","authenticated-orcid":false,"given":"Joan","family":"Serra-Sagrist\u00e0","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, Universitat Aut\u00f2noma de Barcelona, 08193 Bellaterra, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14118","DOI":"10.1109\/ACCESS.2018.2812999","article-title":"Modern trends in hyperspectral image analysis: A review","volume":"6","author":"Khan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"(2022, May 01). 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