[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127830-en":3,"doc-seo-127830-105":31,"detail-sidebar-cat-0-en-105":92},{"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},127830,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Adaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization","Earth observation depends on high-volume synthetic aperture radar (SAR) data for climate monitoring, disaster response, asset management, and security, yet modern SAR downlink capacity is often outpaced by the captured data volume. The Adaptive SAR Signal Compression Through Artificial Intelligence (SARAI) project addresses on-board bottlenecks by using machine learning to infer statistical features from raw complex signals and select optimal compression algorithms and bitrate encodings. Results demonstrate average encoding savings of 0.3 bits per block while preserving comparable quantization-noise performance in the image domain.","SSC24-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 informed the selection of relevant applications and frequency bandwidths for detailed study. A subsequent analysis and trade-off of current SAR compression algorithms involved evaluating these algorithms against various metrics such as applicability to specific bands, applications and complexity measures.  \nThe project then assessed the feasibility and capabilities of machine learning in this context. A software prototype was developed to select algorithms and algorithm parameters based on statistical features of the raw data and compress data with different algorithms through the scene. Bit rate allocations were varied in Block Adaptive Quantization (BAQ) and FFT-BAQ algorithms. The results showed improvements over fixed bitrates, saving an average of 0.3 bits per block in the encoding, whilst maintaining similar signal-to-quantization-noise measurements in the image domain as the full BAQ 4-bit rate. Finally, there was an examination of hardware requirements and constraints. This was crucial in understanding the practical aspects of implementing ML-based compression algorithms in real-world SAR applications.  \nIn conclusion, this project has laid a foundational framework for the future integration of machine learning in raw SAR data compression, offering a path towards more efficient and adaptive compression techniques that can significantly enhance the performance of SAR systems in ","cbCaikLLX1tgx0np","https://ap.wps.com/l/cbCaikLLX1tgx0np","pdf",1350720,4,1,18,"English","en",105,"# Abstract\n# Introduction\n## Background: SmallSat and ESA Sentinel-1\n## Motivation: All-weather imaging and new SAR markets\n## Project approach: ML-enhanced raw compression\n# Project workflow\n## Survey of raw SAR datasets\n## Evaluation and trade-offs of compression algorithms\n## ML feasibility and software prototype\n# Results and system considerations\n## Bitrate allocation in Block Adaptive Quantization (BAQ) and FFT-BAQ\n## Hardware constraints for low-power onboard ML\n# Conclusion","[{\"question\":\"What problem does the Adaptive SARAI project target?\",\"answer\":\"The project targets on-board data bottlenecks caused by modern SAR instruments generating more data than satellite downlink capacity can support.\"},{\"question\":\"How does the project use machine learning in the compression process?\",\"answer\":\"Machine learning models infer information from statistical features in raw complex radar signals and select optimal compression algorithms and encoding bitrate variations based on that inferred content.\"},{\"question\":\"What compression improvements were reported?\",\"answer\":\"Encoding using ML-enhanced bitrate allocation achieved an average saving of 0.3 bits per block compared with fixed bitrates, while maintaining similar signal-to-quantization-noise measurements in the image domain to the full BAQ 4-bit rate.\"}]","Adaptive Raw SAR Data Compression Using Machine Learning Enhanced Block Adaptive Quantization | 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problem does the Adaptive SARAI project target?","Question",{"text":76,"@type":77},"The project targets on-board data bottlenecks caused by modern SAR instruments generating more data than satellite downlink capacity can support.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the project use machine learning in the compression process?",{"text":81,"@type":77},"Machine learning models infer information from statistical features in raw complex radar signals and select optimal compression algorithms and encoding bitrate variations based on that inferred content.",{"name":83,"@type":74,"acceptedAnswer":84},"What compression improvements were reported?",{"text":85,"@type":77},"Encoding using ML-enhanced bitrate allocation achieved an average saving of 0.3 bits per block compared with fixed bitrates, while maintaining similar signal-to-quantization-noise measurements in the image domain to the full BAQ 4-bit 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