{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:13:58Z","timestamp":1780676038243,"version":"3.54.1"},"reference-count":105,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"Narodowe Centrum Bada\u0144 i Rozwoju","doi-asserted-by":"publisher","award":["POIR.01.01.01-00-0356\/17"],"award-info":[{"award-number":["POIR.01.01.01-00-0356\/17"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["BEETLES"],"award-info":[{"award-number":["BEETLES"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007835","name":"Politechnika \u015al\u0105ska","doi-asserted-by":"publisher","award":["02\/080\/RGJ20\/0003"],"award-info":[{"award-number":["02\/080\/RGJ20\/0003"]}],"id":[{"id":"10.13039\/501100007835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings.<\/jats:p>","DOI":"10.3390\/rs13081532","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"1532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-1569","authenticated-orcid":false,"given":"Jakub","family":"Nalepa","sequence":"first","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"},{"name":"Faculty of Automatic Control, Electronics and Computer Science, Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9265-3997","authenticated-orcid":false,"given":"Michal","family":"Myller","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Cwiek","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukasz","family":"Zak","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomasz","family":"Lakota","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukasz","family":"Tulczyjew","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3669-5110","authenticated-orcid":false,"given":"Michal","family":"Kawulok","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"},{"name":"Faculty of Automatic Control, Electronics and Computer Science, Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"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","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/LGRS.2018.2871273","article-title":"Sparse representation-based hyperspectral image classification using multiscale superpixels and guided filter","volume":"16","author":"Dundar","year":"2018","journal-title":"IEEE Geosci. 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