[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128360-en":3,"doc-seo-128360-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128360,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Adaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization","Earth observation (EO) depends on timely, reliable sensing, and synthetic aperture radar (SAR) is valuable because it produces dense time series without sensitivity to cloud cover or darkness. Growth in commercial SAR is driving rapidly increasing data volumes that can exceed satellite downlink capacity, creating an urgent need for efficient onboard data compression. The SARAI project applies machine learning to raw complex radar signals by extracting statistical features, inferring content, and selecting compression algorithms and bitrate allocations to conserve bandwidth while improving system performance. Results indicate average savings of 0.3 bits per block versus fixed bitrate approaches while preserving comparable signal-to-quantization-noise behavior.","Edinburgh Research Explorer  \nAdaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization  \nCitation for published version:  \nHay, C, Anderson, C, Donnell, L, Ireland, M & Yaghoobi Vaighan, M 2024, 'Adaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization', Paper presented at Small Satellite Conference , United States, 5/08/24-8/08/24 .  \n\u003C[https://digitalcommons.usu.edu/smallsat/2024/all2024/84/](https://digitalcommons.usu.edu/smallsat/2024/all2024/84/)>  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 25. Nov. 2025  \nSSC24-IV-09  \nAdaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive  \nQuantization  \nCraig Hay, Cameron Anderson, Lucy Donnell, Murray Ireland  \nCraft Prospect Ltd  \nSuite 12, Fairfield, 1048 Govan Road, Glasgow G51 4XS, UK; +44 7421 994 712  \n[craig@craftprospect.com](craig@craftprospect.com)  \nMehrdad Yaghoobi  \nSchool of Engineering, University of Edinburgh  \n204 AGB Building, EH9 3FG, United Kingdom; +44 131 650 7185  \n[m.yaghoobi-vaighan@ed.ac.uk](m.yaghoobi-vaighan@ed.ac.uk)  \nABSTRACT  \nEarth observation (EO) data plays a crucial role in climate monitoring, disaster response, asset management, and security, with synthetic aperture radar (SAR) data being particularly valuable. The ability of SAR to generate dense time series, unaffected by cloud cover or darkness, makes it ideal for monitoring applications. The commercial SAR sector has experienced significant recent growth, providing increasing amounts of data through advancements in quantity, quality, frequency, and dissemination. However, challenges in data management have emerged due to the volume of data captured by modern SAR instruments, surpassing satellite downlink capacities.  \nThe Adaptive SAR Signal Compression Through Artificial Intelligence (SARAI) project, funded by the European Space Agency, adopted a unique approach to solving this on-board data bottleneck by focusing on enhancing the effectiveness of raw data compression using machine learning (ML) models. These models make inferences based on statistical features in the raw complex radar signals and select optimal compression algorithms based on content inferred from raw SAR data. The results are used to vary the encoding bitrate within algorithms and select between different algorithms, conserving bandwidth and improving system performance. This innovative strategy demonstrates the feasibility of extracting valuable insights directly from raw SAR data, a pioneering step in satellite data processing.  \nTraditionally, on-board processing of SAR data into images faces computational complexity challenges. This project's breakthrough lies in inferring information from data without creating SAR images and can be extended to applications such as target and change detection in the future. Implementing an ML model within the constrained, low-power computing environment of a satellite poses unique challenges. The model must be trained on representative data and operate effectively within these limitations.  \nThe initial phase of the project involved a survey of publicly available raw SAR datasets, which info","cbCaisCS770idmo6","https://ap.wps.com/l/cbCaisCS770idmo6","pdf",1431804,6,1,19,"English","en",105,"# Abstract\n## Project motivation and problem\n## SARAI machine-learning compression approach\n## System constraints and ML feasibility\n## Dataset survey and algorithm trade-offs\n## Prototype results and hardware considerations\n## Conclusion\n# Introduction\n## SAR relevance in SmallSats\n## ESA Sentinel-1 impact on global coverage","[{\"question\":\"Why is SAR data compression important for modern SAR missions?\",\"answer\":\"SAR instruments generate data volumes that can exceed satellite downlink capacity, so efficient onboard compression is needed to manage bandwidth without losing essential information.\"},{\"question\":\"How does the SARAI approach use machine learning for compression?\",\"answer\":\"The ML model derives inferences from statistical features in raw complex radar signals and then selects optimal compression algorithms, varying encoding bitrate to match inferred content.\"},{\"question\":\"What performance improvement was reported compared with fixed bitrates?\",\"answer\":\"Encoding with Block Adaptive Quantization variations showed an average saving of about 0.3 bits per block while maintaining similar signal-to-quantization-noise measurements in the image domain as the full 4-bit BAQ rate.\"}]","Adaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization | 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is SAR data compression important for modern SAR missions?","Question",{"text":77,"@type":78},"SAR instruments generate data volumes that can exceed satellite downlink capacity, so efficient onboard compression is needed to manage bandwidth without losing essential information.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the SARAI approach use machine learning for compression?",{"text":82,"@type":78},"The ML model derives inferences from statistical features in raw complex radar signals and then selects optimal compression algorithms, varying encoding bitrate to match inferred content.",{"name":84,"@type":75,"acceptedAnswer":85},"What performance improvement was reported compared with fixed bitrates?",{"text":86,"@type":78},"Encoding with Block Adaptive Quantization variations showed an average saving of about 0.3 bits per block while maintaining similar signal-to-quantization-noise measurements in the image domain as the full 4-bit BAQ 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