{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:24:31Z","timestamp":1760239471374,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T00:00:00Z","timestamp":1605225600000},"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>Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.<\/jats:p>","DOI":"10.3390\/rs12223741","type":"journal-article","created":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T10:32:47Z","timestamp":1605263567000},"page":"3741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7641-4643","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Caba","sequence":"first","affiliation":[{"name":"School of Computer Science, University of Castilla-La Mancha (UCLM), 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1931-3245","authenticated-orcid":false,"given":"Jes\u00fas","family":"Barba","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Castilla-La Mancha (UCLM), 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-4633","authenticated-orcid":false,"given":"Jose A. de la Torre and\u00a0Sebasti\u00e1n","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Castilla-La Mancha (UCLM), 13071 Ciudad Real, Spain"},{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Noor, N.R.M., and Vladimirova, T. (2011). Integer KLT design space exploration for hyperspectral satellite image compression. International Conference on Hybrid Information Technology, Springer.","DOI":"10.1007\/978-3-642-24082-9_80"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Radosavljevi\u0107, M., Brklja\u010d, B., Lugonja, P., Crnojevi\u0107, V., Trpovski, \u017d., Xiong, Z., and Vukobratovi\u0107, D. (2020). Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study. Remote Sens., 12.","DOI":"10.3390\/rs12101590"},{"key":"ref_4","first-page":"19","article-title":"Limitations of hyperspectral earth observation on small satellites","volume":"1","author":"Villafranca","year":"2012","journal-title":"J. Small Satell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Valentino, R., Jung, W.S., and Ko, Y.B. (2018). A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks. Sensors, 18.","DOI":"10.3390\/s18113751"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1109\/JPROC.2012.2231391","article-title":"The promise of reconfigurable computing for hyperspectral imaging onboard systems: A review and trends","volume":"101","author":"Lopez","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"17E517","DOI":"10.1063\/1.4918638","article-title":"Micromagnetics on high-performance workstation and mobile computational platforms","volume":"117","author":"Fu","year":"2015","journal-title":"J. Appl. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1201\/b11222-5","article-title":"Hyperspectral sensor characteristics: Airborne, spaceborne, hand-held, and truck-mounted; Integration of hyperspectral data with Lidar","volume":"4","author":"Ortenberg","year":"2011","journal-title":"Hyperspectral Remote Sens. Veg."},{"key":"ref_10","unstructured":"Board, N.S., and Council, N.R. (2005). Autonomous Vehicles in Support of Naval Operations, National Academies Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s12517-017-2989-x","article-title":"Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping","volume":"10","author":"Green","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Keymeulen, D., Aranki, N., Hopson, B., Kiely, A., Klimesh, M., and Benkrid, K. (2012, January 3\u201310). GPU lossless hyperspectral data compression system for space applications. Proceedings of the 2012 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2012.6187255"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, B. (2011). Satellite Data Compression, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-1183-3"},{"key":"ref_15","unstructured":"Consultative Committee for Space Data Systems (CCSDS) (2020, July 10). Image Data Compression.CCSDS, Green Book 120.1-G-2. Available online: https:\/\/public.ccsds.org\/Pubs\/120x1g2.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Motta, G., Rizzo, F., and Storer, J.A. (2006). Hyperspectral Data Compression, Springer Science & Business Media.","DOI":"10.1007\/0-387-28600-4"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/TGRS.2007.894565","article-title":"Transform coding techniques for lossy hyperspectral data compression","volume":"45","author":"Penna","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Marcellin, M.W., and Taubman, D.S. (2002). JPEG2000: Image compression fundamentals, standards, and practice. International Series in Engineering and Computer Science, Secs 642, Springer."},{"key":"ref_19","unstructured":"Chang, L., Cheng, C.M., and Chen, T.C. (2000, January 2\u20134). An efficient adaptive KLT for multispectral image compression. Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, TX, USA."},{"key":"ref_20","unstructured":"Hao, P., and Shi, Q. (2003, January 14\u201317). Reversible integer KLT for progressive-to-lossless compression of multiple component images. Proceedings of the 2003 International Conference on Image Processing (Cat. No. 03CH37429), Barcelona, Spain."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abrardo, A., Barni, M., and Magli, E. (2011, January 22\u201327). Low-complexity predictive lossy compression of hyperspectral and ultraspectral images. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5946524"},{"key":"ref_22","unstructured":"Kiely, A.B., Klimesh, M., Blanes, I., Ligo, J., Magli, E., Aranki, N., Burl, M., Camarero, R., Cheng, M., and Dolinar, S. (2018, January 20\u201321). The new CCSDS standard for low-complexity lossless and near-lossless multispectral and hyperspectral image compression. Proceedings of the 2018 Onboard Payload Data Compression Workshop, Matera, Italy."},{"key":"ref_23","unstructured":"Auge, E., Santalo, J., Blanes, I., Serra-Sagrista, J., and Kiely, A. (2011, January 21\u201324). Review and implementation of the emerging CCSDS recommended standard for multispectral and hyperspectral lossless image coding. Proceedings of the 2011 First International Conference on Data Compression, Communications and Processing, Palinuro, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"074594","DOI":"10.1117\/1.JRS.7.074594","article-title":"Performance impact of parameter tuning on the CCSDS-123 lossless multi-and hyperspectral image compression standard","volume":"7","author":"Kiely","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1109\/JSTARS.2013.2247975","article-title":"Highly-parallel GPU architecture for lossy hyperspectral image compression","volume":"6","author":"Santos","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"54269","DOI":"10.1109\/ACCESS.2020.2980767","article-title":"SHyLoC 2.0: A Versatile Hardware Solution for On-Board Data and Hyperspectral Image Compression on Future Space Missions","volume":"8","author":"Barrios","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Santos, L., L\u00f3pez, J.F., Sarmiento, R., and Vitulli, R. (2013, January 24\u201327). FPGA implementation of a lossy compression algorithm for hyperspectral images with a high-level synthesis tool. Proceedings of the 2013 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS-2013), Torino, Italy.","DOI":"10.1109\/AHS.2013.6604233"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"D\u00edaz, M., Guerra, R., Horstrand, P., Martel, E., L\u00f3pez, S., L\u00f3pez, J.F., and Roberto, S. (2019). Real-Time Hyperspectral Image Compression Onto Embedded GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1\u201318.","DOI":"10.1109\/JSTARS.2019.2917088"},{"key":"ref_30","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_31","first-page":"1159","article-title":"An algorithm for an accurate detection of anomalies in hyperspectral images with a low computational complexity","volume":"56","author":"Guerra","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"D\u00edaz, M., Guerra, R., Horstrand, P., L\u00f3pez, S., and Sarmiento, R. (2019). A Line-by-Line Fast Anomaly Detector for Hyperspectral Imagery. IEEE Trans. Geosci. Remote Sens., 8968\u20138982.","DOI":"10.1109\/TGRS.2019.2923921"},{"key":"ref_33","unstructured":"Diaz, M., Guerra Hern\u00e1ndez, R., and Lopez, S. A Novel Hyperspectral Target Detection Algorithm For Real-Time Applications With Push-Broom Scanners. Proceedings of the 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"D\u00edaz, M., Guerra, R., Horstrand, P., L\u00f3pez, S., L\u00f3pez, J.F., and Sarmiento, R. (2020). Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations. Remote Sens., 12.","DOI":"10.3390\/rs12081343"},{"key":"ref_35","unstructured":"Consultative Committee for Space Data Systems (CCSDS) (2019, March 11). Blue Books: Recommended Standards. Available online: https:\/\/public.ccsds.org\/Publications\/BlueBooks.aspx."},{"key":"ref_36","unstructured":"Howard, P.G., and Vitter, J.S. (April, January 30). Fast and Efficient Lossless Image Compression. Proceedings of the DCC \u201893: Data Compression Conference, Snowbird, UT, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Guerra Hern\u00e1ndez, R., Barrios, Y., Diaz, M., Baez, A., Lopez, S., and Sarmiento, R. (2019). A Hardware-Friendly Hyperspectral Lossy Compressor for Next-Generation Space-Grade Field Programmable Gate Arrays. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1\u201317.","DOI":"10.1109\/JSTARS.2019.2919791"},{"key":"ref_38","unstructured":"Oberstar, E.L. (2007). Fixed-point representation & fractional math. Oberstar Consult., 9, Available online: https:\/\/www.superkits.net\/whitepapers\/Fixed%20Point%20Representation%20&%20Fractional%20Math.pdf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6045","DOI":"10.1364\/AO.51.006045","article-title":"Dynamic range and sensitivity requirements of satellite ocean color sensors: Learning from the past","volume":"51","author":"Hu","year":"2012","journal-title":"Appl. Opt."},{"key":"ref_40","unstructured":"Kumar, A., Mehta, S., Paul, S., Parmar, R., and Samudraiah, R. Dynamic Range Enhancement of Remote Sensing Electro-Optical Imaging Systems. In Proceeding of the Symposium at Indian Society of Remote Sensing, Bhopal, India."},{"key":"ref_41","unstructured":"Xilinx Inc (2020). UltraFast Vivado HLS Methodology Guide, Xilinx Inc.. Technical Report."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TCAD.2011.2110592","article-title":"High-Level Synthesis for FPGAs: From Prototyping to Deployment","volume":"30","author":"Cong","year":"2011","journal-title":"IEEE Trans. Comput.-Aided Design Integr. Circuits Syst."},{"key":"ref_43","unstructured":"Bailey, D.G. (, January September). The Advantages and Limitations of High Level Synthesis for FPGA Based Image Processing. Proceedings of the 9th International Conference on Distributed Smart Cameras."},{"key":"ref_44","unstructured":"de Fine Licht, J., Meierhans, S., and Hoefler, T. (2018). Transformations of High-Level Synthesis Codes for High-Performance Computing. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","article-title":"MapReduce: Simplified Data Processing on Large Clusters","volume":"51","author":"Dean","year":"2008","journal-title":"Commun. ACM"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.vlsi.2019.06.011","article-title":"Testing framework for on-board verification of HLS modules using grey-box technique and FPGA overlays","volume":"68","author":"Caba","year":"2019","journal-title":"Integration"},{"key":"ref_47","first-page":"446","article-title":"Rapid Prototyping and Verification of Hardware Modules Generated Using HLS","volume":"Volume 10824","author":"Caba","year":"2018","journal-title":"Proceedings of the Applied Reconfigurable Computing, Architectures, Tools, and Applications-14th International Symposium, ARC 2018, Santorini, Greece, 2\u20134 May 2018"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"66919","DOI":"10.1109\/ACCESS.2019.2913957","article-title":"A UAV platform based on a hyperspectral sensor for image capturing and on-board processing","volume":"7","author":"Horstrand","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"NVIDIA Corporation (2019, September 07). NVIDIA Jetson Linux Developer Guide 32.4.3 Release. Power Management for Jetson Nano and Jetson TX1 Devices. Available online: https:\/\/docs.nvidia.com\/jetson\/l4t\/index.html#page\/Tegra%20Linux%20Driver%20Package%20Development%20Guide\/power_management_nano.html."},{"key":"ref_50","unstructured":"NVIDIA Corporation (2019, September 07). NVIDIA Jetson Linux Driver Package Software Features Release 32.3. Power Management for Jetson TX2 Series Devices. Available online: https:\/\/docs.nvidia.com\/jetson\/archives\/l4t-archived\/l4t-3231\/index.html#page\/Tegra%20Linux%20Driver%20Package%20Development%20Guide\/power_management_tx2_32.html."},{"key":"ref_51","unstructured":"NVIDIA Corporation (2019, September 07). NVIDIA Jetson Linux Developer Guide 32.4.3 Release. Power Management for Jetson Xavier NX and Jetson AGX Xavier Series Device. Available online: https:\/\/docs.nvidia.com\/jetson\/l4t\/index.html#page\/Tegra%20Linux%20Driver%20Package%20Development%20Guide\/power_management_jetson_xavier.html."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3741\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:33:13Z","timestamp":1760178793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3741"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,13]]},"references-count":51,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223741"],"URL":"https:\/\/doi.org\/10.3390\/rs12223741","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,11,13]]}}}