88582 20210902121706.0 doi 10.3390/rs12030534 sideral 117324 ART-2020-117324 eng Alcolea, Adrián Universidad de Zaragoza (orcid)0000-0002-7057-4283 Inference in supervised spectral classifiers for on-board hyperspectral imaging: An overview 2020 Access copy available to the general public Unrestricted Machine learning techniques are widely used for pixel-wise classification of hyperspectral images. These methods can achieve high accuracy, but most of them are computationally intensive models. This poses a problem for their implementation in low-power and embedded systems intended for on-board processing, in which energy consumption and model size are as important as accuracy. With a focus on embedded anci on-board systems (in which only the inference step is performed after an off-line training process), in this paper we provide a comprehensive overview of the inference properties of the most relevant techniques for hyperspectral image classification. For this purpose, we compare the size of the trained models and the operations required during the inference step (which are directly related to the hardware and energy requirements). Our goal is to search for appropriate trade-offs between on-board implementation (such as model size anci energy consumption) anci classification accuracy. info:eu-repo/grantAgreement/ES/DGA/T58-17R info:eu-repo/grantAgreement/ES/MEC/FPU15-02090 info:eu-repo/grantAgreement/ES/MINECO/TIN2016-76635-C2-1-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 4.848 2020 GEOSCIENCES, MULTIDISCIPLINARY 27 / 198 = 0.136 2020 Q1 T1 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY 8 / 29 = 0.276 2020 Q2 T1 REMOTE SENSING 10 / 32 = 0.312 2020 Q2 T1 ENVIRONMENTAL SCIENCES 76 / 273 = 0.278 2020 Q2 T1 1.284 2020 Earth and Planetary Sciences (miscellaneous) 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Paoletti, Mercedes E. Haut, Juan M. Resano, Javier Universidad de Zaragoza (orcid)0000-0002-7532-2720 Plaza, Antonio 5007 035 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Arquit.Tecnología Comput. 12, 3 (2020), 534 [29 pp.] Remote sens. (Basel) Remote Sensing 2072-4292 1237935 http://zaguan.unizar.es/record/88582/files/texto_completo.pdf Versión publicada 492651 http://zaguan.unizar.es/record/88582/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:88582 articulos driver 2021-09-02-09:16:58 ARTICLE