{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:16Z","timestamp":1760241916177,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,12]],"date-time":"2018-11-12T00:00:00Z","timestamp":1541980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware\/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo3 framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo3-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth.<\/jats:p>","DOI":"10.3390\/rs10111790","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T02:42:41Z","timestamp":1542163361000},"page":"1790","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6484-9199","authenticated-orcid":false,"given":"Alberto","family":"Ortiz","sequence":"first","affiliation":[{"name":"Centro de Electr\u00f3nica Industrial, Universidad Polit\u00e9cnica de Madrid, Jos\u00e9 Guti\u00e9rrez Abascal 2, 28006 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-743X","authenticated-orcid":false,"given":"Alfonso","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Centro de Electr\u00f3nica Industrial, Universidad Polit\u00e9cnica de Madrid, Jos\u00e9 Guti\u00e9rrez Abascal 2, 28006 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4303-3051","authenticated-orcid":false,"given":"Ra\u00fal","family":"Guerra","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2360-6721","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4995-7009","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Otero","sequence":"additional","affiliation":[{"name":"Centro de Electr\u00f3nica Industrial, Universidad Polit\u00e9cnica de Madrid, Jos\u00e9 Guti\u00e9rrez Abascal 2, 28006 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4843-0507","authenticated-orcid":false,"given":"Roberto","family":"Sarmiento","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5697-0573","authenticated-orcid":false,"given":"Eduardo","family":"De la Torre","sequence":"additional","affiliation":[{"name":"Centro de Electr\u00f3nica Industrial, Universidad Polit\u00e9cnica de Madrid, Jos\u00e9 Guti\u00e9rrez Abascal 2, 28006 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Transon, J., D\u2019Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of hyperspectral Earth Observation applications from space in the Sentinel-2 context. Remote Sens., 10.","DOI":"10.3390\/rs10020157"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Fabelo, H., Ortega, S., Kabwama, S.M., Callico, G., Bulters, D., Szolna, A., F. Pineiro, J., and Sarmiento, R. (2016). HELICoiD project: A new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations. Proc. SPIE, 9860.","DOI":"10.1117\/12.2223075"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Plaza, A.J., and Chang, C.I. (2007). High Performance Computing in Remote Sensing, CRC Press.","DOI":"10.1201\/9781420011616"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/JSTARS.2017.2755639","article-title":"GPU Parallel Implementation of Spatially Adaptive Hyperspectral Image Classification","volume":"11","author":"Wu","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1109\/LGRS.2018.2810600","article-title":"Multicore Real-Time Implementation of a Full Hyperspectral Unmixing Chain","volume":"15","author":"Plaza","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/JSTARS.2015.2485399","article-title":"A Hybrid CPU\u2013GPU Real-Time Hyperspectral Unmixing Chain","volume":"9","author":"Torti","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4334","DOI":"10.1109\/JSTARS.2015.2504427","article-title":"FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images","volume":"9","author":"Mozos","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/JSTARS.2017.2767680","article-title":"FPGA Implementation of the CCSDS 1.2.3 Standard for Real-Time Hyperspectral Lossless Compression","volume":"11","author":"Mozos","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","unstructured":"Tsigkanos, A., Kranitis, N., Theodorou, G.A., and Paschalis, A. (2018). A 3.3 Gbps CCSDS 123.0-B-1 Multispectral amp; Hyperspectral Image Compression Hardware Accelerator on a Space-Grade SRAM FPGA. IEEE Trans. Emerg. Top. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/JPROC.2018.2806218","article-title":"Modern Small Satellites-Changing the Economics of Space","volume":"106","author":"Sweeting","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.actaastro.2011.12.014","article-title":"A survey and assessment of the capabilities of Cubesats for Earth observation","volume":"74","author":"Selva","year":"2012","journal-title":"Acta Astronaut."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MWC.2016.7422411","article-title":"Virtual multi-beamforming for distributed satellite clusters in space information networks","volume":"23","author":"Yu","year":"2016","journal-title":"IEEE Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.paerosci.2016.11.002","article-title":"CubeSat evolution: Analyzing CubeSat capabilities for conducting science missions","volume":"88","author":"Poghosyan","year":"2017","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/JPROC.2018.2793158","article-title":"Energy Storage Technologies for Small Satellite Applications","volume":"106","author":"Chin","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JPROC.2018.2802438","article-title":"Onboard Processing With Hybrid and Reconfigurable Computing on Small Satellites","volume":"106","author":"George","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, A., Suriano, L., Otero, A., and de la Torre, E. (2017, January 24\u201327). Dynamic reconfiguration under RTEMS for fault mitigation and functional adaptation in SRAM-based SoPCs for space systems. Proceedings of the 2017 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), Pasadena, CA, USA.","DOI":"10.1109\/AHS.2017.8046357"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"D\u00f6rflinger, A., Fiethe, B., Michalik, H., Fekete, S.P., Keldenich, P., and Scheffer, C. (2017, January 24\u201327). Resource-efficient dynamic partial reconfiguration on FPGAs for space instruments. Proceedings of the 2017 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), Pasadena, CA, USA.","DOI":"10.1109\/AHS.2017.8046355"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, A., Valverde, J., Portilla, J., Otero, A., Riesgo, T., and de la Torre, E. (2018). FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo3 Framework. Sensors, 18.","DOI":"10.3390\/s18061877"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6752","DOI":"10.1109\/TGRS.2015.2447573","article-title":"A New Fast Algorithm for Linearly Unmixing Hyperspectral Images","volume":"53","author":"Guerra","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/JSTSP.2010.2096798","article-title":"Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution","volume":"5","author":"Villa","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1109\/JSTARS.2017.2707541","article-title":"Parallel Implementation of a Full Hyperspectral Unmixing Chain Using OpenCL","volume":"10","author":"Botella","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ke, J., Guo, Y., and Sowmya, A. (2017, January 21\u201326). A Fast Approximate Spectral Unmixing Algorithm Based on Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.38"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s11554-012-0276-3","article-title":"Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs","volume":"9","author":"Plaza","year":"2014","journal-title":"J. Real-Time Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s11554-012-0269-2","article-title":"Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs","volume":"10","author":"Ramalho","year":"2015","journal-title":"J. Real-Time Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/JSTARS.2016.2614842","article-title":"A GPU-Based Processing Chain for Linearly Unmixing Hyperspectral Images","volume":"10","author":"Martel","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4879","DOI":"10.1109\/JSTARS.2017.2737958","article-title":"On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach","volume":"10","author":"Guerra","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/TGRS.2016.2603527","article-title":"Predictive Lossless Compression of Regions of Interest in Hyperspectral Images With No-Data Regions","volume":"55","author":"Shen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.neucom.2014.06.052","article-title":"Compression of hyperspectral remote sensing images by tensor approach","volume":"147","author":"Zhang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guerra, R., Barrios, Y., D\u00edaz, M., Santos, L., L\u00f3pez, S., and Sarmiento, R. (2018). A New Algorithm for the On-Board Compression of Hyperspectral Images. Remote Sens., 10.","DOI":"10.3390\/rs10030428"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Giordano, R., and Guccione, P. (2017). ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU. Sensors, 17.","DOI":"10.3390\/s17051160"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"24926","DOI":"10.3390\/s151024926","article-title":"Onboard Image Processing System for Hyperspectral Sensor","volume":"15","author":"Hihara","year":"2015","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"05001","DOI":"10.1051\/matecconf\/20164505001","article-title":"High Efficiency On-Board Hyperspectral Image Classification with Zynq SoC","volume":"45","author":"Ma","year":"2016","journal-title":"MATEC Web Conf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/LGRS.2016.2574886","article-title":"Onboard Processing for Data Volume Reduction in High-Resolution Wide-Swath SAR","volume":"13","author":"Villano","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, C., Li, B., Chen, L., Wei, C., Xie, Y., Chen, H., and Yu, W. (2017). A Spaceborne Synthetic Aperture Radar Partial Fixed-Point Imaging System Using a Field-Programmable Gate Array Application-Specific Integrated Circuit Hybrid Heterogeneous Parallel Acceleration Technique. Sensors, 17.","DOI":"10.3390\/s17071493"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jallad, A.M., and Mohammed, L.B. (2014, January 14\u201318). Hardware Support Vector Machine (SVM) for satellite on-board applications. Proceedings of the 2014 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), Leicester, UK.","DOI":"10.1109\/AHS.2014.6880185"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1145\/2740070.2631454","article-title":"OpenSAN: A Software-defined Satellite Network Architecture","volume":"44","author":"Bao","year":"2014","journal-title":"SIGCOMM Comput. Commun. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tang, C., Hu, X., Zhou, S., Liu, L., Pan, J., Chen, L., Guo, R., Zhu, L., Hu, G., Li, X., He, F., and Chang, Z. (2018). Initial results of centralized autonomous orbit determination of the new-generation BDS satellites with inter-satellite link measurements. J. Geod.","DOI":"10.1007\/s00190-018-1113-7"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hofmann, A., Glein, R., Frank, L., Wansch, R., and Heuberger, A. (2017, January 15\u201318). Reconfigurable on-board processing for flexible satellite communication systems using FPGAs. Proceedings of the 2017 Topical Workshop on Internet of Space (TWIOS), Phoenix, AZ, USA.","DOI":"10.1109\/TWIOS.2017.7869767"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Glumb, R., Lapsley, M., Mantica, P., Maurer, P., Reinhard, J., and Kirsch, T. (2017). High-Performance On-board Signal Processing for Interferometric CubeSats. AIAA SPACE and Astronautics Forum and Exposition, AIAA SPACE Forum, American Institute of Aeronautics and Astronautics.","DOI":"10.2514\/6.2017-5188"},{"key":"ref_40","unstructured":"Manning, J., Langerman, D., Ramesh, B., Gretok, E., Wilson, C., George, A., MacKinnon, J., and Crum, G. (2018, January 4\u20139). Machine-Learning Space Applications on SmallSat Platforms with TensorFlow. Proceedings of the 32nd Annual AIAA\/USU Conference on Small Satellites, Logan, UT, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Winter, M.E. (1999). N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proc. SPIE, 3753.","DOI":"10.1117\/12.366289"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TGRS.2005.844293","article-title":"Vertex component analysis: A fast algorithm to unmix hyperspectral data","volume":"43","author":"Nascimento","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1109\/TIP.2014.2363423","article-title":"Spectral Unmixing via Data-Guided Sparsity","volume":"23","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shames, P. (2002, January 10\u201319). A Message Transfer Service for Space Applications. Proceedings of the SpaceOps 2002 Conference, Houston, TX, USA.","DOI":"10.2514\/6.2002-T5-15"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hablot, L., Gluck, O., Mignot, J.C., Genaud, S., and Primet, P.V.B. (2007, January 17\u201320). Comparison and tuning of MPI implementations in a grid context. Proceedings of the 2007 IEEE International Conference on Cluster Computing, Austin, TX, USA.","DOI":"10.1109\/CLUSTR.2007.4629265"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1790\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:16Z","timestamp":1760196556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/11\/1790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,12]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["rs10111790"],"URL":"https:\/\/doi.org\/10.3390\/rs10111790","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,11,12]]}}}