Relevant bibliographies by topics / SAR raw data compression

Academic literature on the topic 'SAR raw data compression'

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Published: 14 March 2023

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Journal articles on the topic "SAR raw data compression"

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Parkes, S. M., and H. L. Clifton. "The compression of raw SAR and SAR image data." International Journal of Remote Sensing 20, no. 18 (January 1999): 3563–81. http://dx.doi.org/10.1080/014311699211200.

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Pascazio, V., and G. Schirinzi. "SAR raw data compression by subband coding." IEEE Transactions on Geoscience and Remote Sensing 41, no. 5 (May 2003): 964–76. http://dx.doi.org/10.1109/tgrs.2003.811811.

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Moureaux, J. M., P. Gauthier, M. Barlaud, and P. Bellemain. "Raw SAR data compression using vector quantization." International Journal of Remote Sensing 16, no. 16 (November 10, 1995): 3179–87. http://dx.doi.org/10.1080/01431169508954621.

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Hua, Bin, Haiming Qi, Ping Zhang, and Xin Li. "Vector quantization for saturated SAR raw data compression." Advances in Space Research 45, no. 11 (June 2010): 1330–37. http://dx.doi.org/10.1016/j.asr.2010.01.007.

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Zeng, Shang Chun, Yun Xia Xie, Yi Xian Chen, and Zhao Da Zhu. "Study on an Algorithm for SAR Raw Data Compression." Applied Mechanics and Materials 380-384 (August 2013): 1495–98. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1495.

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t is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference. The algorithm proposed in this paper has a certain practical value.
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Zeng, Shang Chun, Xian Lin Deng, Yi Xian Chen, Yun Xia Xie, and Zhao Da Zhu. "A Compression Algorithm for SAR Data after Range Focusing." Advanced Materials Research 694-697 (May 2013): 2877–80. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2877.

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It is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference.
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Pieterse, Chané, Warren P. Plessis, and Richard W. Focke. "Metrics to evaluate compression algorithms for raw SAR data." IET Radar, Sonar & Navigation 13, no. 3 (March 2019): 333–46. http://dx.doi.org/10.1049/iet-rsn.2018.5213.

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Qi, HaiMing, WeiDong Yu, and Xi Chen. "Piecewise linear mapping algorithm for SAR raw data compression." Science in China Series F: Information Sciences 51, no. 12 (August 27, 2008): 2126–34. http://dx.doi.org/10.1007/s11432-008-0133-y.

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Minhui, Zhu, Peng Hailiang, Wu Yirong, and Qi Xuan. "Enhanced multistage vector quantization for SAR raw data compression." Journal of Electronics (China) 13, no. 2 (April 1996): 97–101. http://dx.doi.org/10.1007/bf02684748.

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Xuan, Qi, Zhu Minhui, and Peng Hailiang. "Rapid codebook search algorithm for SAR raw data compression." Journal of Electronics (China) 13, no. 2 (April 1996): 110–15. http://dx.doi.org/10.1007/bf02684750.

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Dissertations / Theses on the topic "SAR raw data compression"

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Yang, Yun-zhi, Shun-ji Huang, and Jian-guo Wang. "The Realization Analysis of SAR Raw Data With Block Adaptive Vector Quantization Algorithm." International Foundation for Telemetering, 2003. http://hdl.handle.net/10150/605596.

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International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada
In this paper, we discuss a Block Adaptive Vector Quantization(BAVQ) Algorithm for Synthetic Aperture Radar(SAR). And we discuss a realization method of BAVQ algorithm for SAR raw data compressing in digital signal processor. Using the algorithm and the digital signal processor, we have compressed the SIR_C/X_SAR data.
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Schmidt, Alfred Rudolf. "Secondary range compression for improved range/Doppler processing of SAR data with high squint." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/26325.

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This thesis examines a new algorithm, to be called secondary range compression (SRC), for significantly improving the range resolution of the range/Doppler synthetic aperture radar (SAR) processing algorithm when the radar antenna is significantly squinted away from the zero Doppler direction. The algorithm was recently introduced by Jin and Wu [13] for application to the SEASAT SAR sensor. Significant extensions of their algorithm and models are presented. First the model of range broadening in the basic range/Doppler algorithm is extended by using a more general form for the range compressed profile. A mathematical theory is developed to examine more closely the approximations involved in both basic range/Doppler processing and SRC and to explore alternate SRC implementations. The theory is used to derive the SRC algorithm as a matched filter directly from the point target response model rather from the simplified range compressed response used by Jin and Wu. Two new discrete implementations (azimuth SRC and range SRC) are developed for both single-look and multilook processing. In addition two new alternate methods of multilook SRC are presented : fixed SRC and look-dependent SRC. The sensitivity of the SRC algorithms to parameter errors is investigated. Extensive simulations are developed to quantify the image quality produced by each algorithm for a variety of processing parameters. The simulation results with nominal RADARSAT parameters show that the SRC algorithms can significantly extend the range of squint angles which can be processed with the range/Doppler type of algorithm.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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McGuinness, Christopher. "Characterizing Remote Sensing Data Compression Distortion for Improved Automated Exploitation Performance." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1524844209730534.

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Wilhelmy, Jochen [Verfasser], and Willi A. [Akademischer Betreuer] Kalender. "Lossless and Lossy Raw Data Compression in CT Imaging / Jochen Wilhelmy. Betreuer: Willi A. Kalender." Erlangen : Universitätsbibliothek der Universität Erlangen-Nürnberg, 2012. http://d-nb.info/1029374414/34.

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Bhattaram, Sneha. "Signal Compression Methods for a Wear Debris Sensor." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1399201029.

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Brunham, Kalen B. "Development of an FPGA based wavelet transformer for the compression of raw SAR data." 2003. http://hdl.handle.net/1993/19838.

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Book chapters on the topic "SAR raw data compression"

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Shangchun, Zeng, Chen Yixian, Xia Ming, Xie Yunxia, and Zhu Zhaoda. "Study on a Compression Algorithm for SAR Raw Data." In Communications in Computer and Information Science, 173–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37149-3_21.

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Sun, Li, Yuedong Zhang, Qun Zhang, and Miao Ma. "DT-CWT-Based Improved BAQ for Anti-Saturation SAR Raw Data Compression." In Lecture Notes in Computer Science, 522–30. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23989-7_53.

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Capizzi, Giacomo, Salvatore Coco, Antonio Laudani, and Giuseppe Pappalardo. "A New Methodology for Synthetic Aperture Radar (SAR) Raw Data Compression Based on Wavelet Transform and Neural Networks." In Lecture Notes in Computer Science, 676–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24844-6_103.

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Yankelevsky, Yael, Arie Feuer, and Zvi Friedman. "Decomposition-Based Compression of Ultrasound Raw-Data." In Latent Variable Analysis and Signal Separation, 522–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_61.

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Jensen, Søren Kejser, Christian Thomsen, and Torben Bach Pedersen. "ModelarDB: Integrated Model-Based Management of Time Series from Edge to Cloud." In Transactions on Large-Scale Data- and Knowledge-Centered Systems LIII, 1–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66863-4_1.

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AbstractTo ensure critical infrastructure is operating as expected, high-quality sensors are increasingly installed. However, due to the enormous amounts of high-frequency time series they produce, it is impossible or infeasible to transfer or even store these time series in the cloud when using state-of-the-practice compression methods. Thus, simple aggregates, e.g., 1–10-minutes averages, are stored instead of the raw time series. However, by only storing these simple aggregates, informative outliers and fluctuations are lost. Many Time Series Management System (TSMS) have been proposed to efficiently manage time series, but they are generally designed for either the edge or the cloud. In this paper, we describe a new version of the open-source model-based TSMS ModelarDB. The system is designed to be modular and the same binary can be efficiently deployed on the edge and in the cloud. It also supports continuously transferring high-frequency time series compressed using models from the edge to the cloud. We first provide an overview of ModelarDB, analyze the requirements and limitations of the edge, and evaluate existing query engines and data stores for use on the edge. Then, we describe how ModelarDB has been extended to efficiently manage time series on the edge, a novel file-based data store, how ModelarDB’s compression has been improved by not storing time series that can be derived from base time series, and how ModelarDB transfers high-frequency time series from the edge to the cloud. As the work that led to ModelarDB began in 2015, we also reflect on the lessons learned while developing it.
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Guo, Yuhua, Shichao Jin, and Yuhong Guo. "Efficient Simulation of Airborne SAR Raw Data in Case of Motion Errors." In Electromagnetic Compatibility [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99378.

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In the simulation of SAR raw data, it is well-known that the frequency-domain algorithm is more efficient than a time-domain algorithm, making it is more suitable for extended scene simulation. However, the frequency-domain algorithm is perhaps better suited for ideal linear motion and requires some degrees of approximations to take the nonlinear motion effects. This chapter presents an efficient simulation approach based on hybrid time and frequency-domain algorithms under certain assumptions. The algorithm has high efficiency and is suitable for the simulation of extended scenes, which demands highly computational resources. The computational complexity of the proposed algorithm is analyzed, followed by numerical results to demonstrate the effectiveness and efficiency of the proposed approach.
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Impagnatiello, Fabrizio, and Andrea Torre. "Frequency Locked Loop System for Doppler Centroid Tracking and automatized Raw Data Correction in Spotlight Real-Time SAR Processors." In Time-Varying Image Processing and Moving Object Recognition, 4, 176–83. Elsevier, 1997. http://dx.doi.org/10.1016/b978-044482307-6/50026-6.

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França, Reinaldo Padilha, Yuzo Iano, Ana Carolina Borges Monteiro, and Rangel Arthur. "Improvement of the Transmission of Information for ICT Techniques Through CBEDE Methodology." In Utilizing Educational Data Mining Techniques for Improved Learning, 13–34. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0010-1.ch002.

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There is no single and universal definition for ICT (information and communication technology). The term is normally adopted to all devices, networking components, applications, and systems that allow combined people, companies, and organizations (i.e., businesses, nonprofit agencies, governments, and enterprises) to interact in the digital world, the modern era. In this digitally globalized world, we deal with a huge set of data all the time. Data refers to facts, events, actions, activities, and transactions which have been and can be recorded (i.e., the raw material from which information is produced), nurturing the infrastructure and components that enable modern computing. Based on this, the present study aims to implement a discrete events-based model with advanced modulation format DQPSK in a simulation environment, the Simulink of MATLAB software, aiming to improve the transmission of information in communication systems. The results show better computational performance related to memory utilization related to compression of the information, showing an improvement of up to 92.6%.
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Arora, Shaifali Madan, and Kavita Khanna. "Block-Based Motion Estimation." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, 651–76. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch029.

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Recent years have witnessed a great technological evolution in video display and capturing technologies leading to the development of new standards of video coding including MPEG-X, H.26X and HEVC. The cost of computations, storage and high bandwidth requirements makes a video data expensive in terms of transmission and storage. This makes video compression absolutely necessary prior to its transmission in order to accommodate for different transmission media's capabilities. Digital video compression technologies therefore have become an important part of the way we create, present, communicate and use visual information. The main aim behind a video compression system is to eliminate the redundancies from a raw video signal. The tradeoff involved in the process of video compression is between the speed, quality and resource utilization. The current chapter explores the techniques, challenges, issues and problems in video compression in detail along with the major advancements in the field.
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França, Reinaldo Padilha, Yuzo Iano, Ana Carolina Borges Monteiro, and Rangel Arthur. "Improved Transmission of Data and Information in Intrusion Detection Environments Using the CBEDE Methodology." In Handbook of Research on Intrusion Detection Systems, 26–46. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch002.

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To anticipate threats, the Intrusion Detection System (IDS) enables the collection and use of information from various types of attacks to defend an entire network infrastructure. Therefore, this chapter develops a method of data transmission based on discrete event concepts, due to the fact that in this digitally globalized world, networks deal with a huge set of data all the time. Data refers to facts, events, actions, activities, and transactions which have been and can be recorded, i.e., the raw material from which information is produced, nurturing the infrastructure and components that enable modern computing. This methodology was named CBEDE and experiments were matched in the MATLAB software, where the memory consumption was evaluated, presenting great potential to intermediate users and computer systems. Results showed better computational performance related to memory utilization related to the compression of the information, showing an improvement reaching up to 114.39%.
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Conference papers on the topic "SAR raw data compression"

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Zou, Bin, Dewu Wang, Ye Zhang, and Zhilu Wu. "Phase Information Reserved Polarimetric SAR Raw Data Compression." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.49.

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Rane, Shantanu, Petros Boufounos, Anthony Vetro, and Yu Okada. "Low complexity efficient raw SAR data compression." In SPIE Defense, Security, and Sensing. SPIE, 2011. http://dx.doi.org/10.1117/12.884034.

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Magli, Enrico, and Gabriella Olmo. "Entropy-constrained predictive compression of SAR raw data." In International Symposium on Optical Science and Technology, edited by Mark S. Schmalz. SPIE, 2003. http://dx.doi.org/10.1117/12.454829.

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Kenge, Akanksha, M. S. Panse, Suyog Choudhari, Hitendra Pethkar, and Raymond Pinto. "Wavelet Transform Based Compression of Raw SAR Data." In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9579774.

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Wang, Min. "Raw SAR data compression by structurally random matrix based compressive sampling." In 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2009. http://dx.doi.org/10.1109/apsar.2009.5374143.

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Li Ting, Wang Dongjin, and Liu Falin. "A Fast BAVQ Algorithm For SAR Raw Data Compression." In 2006 CIE International Conference on Radar. IEEE, 2006. http://dx.doi.org/10.1109/icr.2006.343339.

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Zeng Shangchun, Xie Yunxia, Chen Yixian, and Zhu Zhaoda. "Study on an algorithm for SAR raw data compression." In 2012 4th Electronic System-Integration Technology Conference (ESTC). IEEE, 2012. http://dx.doi.org/10.1109/estc.2012.6485613.

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Agrawal, Navneet, and K. Venugopalan. "Back propagation neural network approach for SAR raw data compression." In MILCOM 2008 - 2008 IEEE Military Communications Conference (MILCOM). IEEE, 2008. http://dx.doi.org/10.1109/milcom.2008.4753106.

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Zeng, Shangchun, Yunxia Xie, Yixian Chen, and Huazhang Wang. "Block adaptive scalar-vector quantization for SAR raw data compression." In Fifth International Conference on Digital Image Processing, edited by Yulin Wang and Xie Yi. SPIE, 2013. http://dx.doi.org/10.1117/12.2030940.

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Qi, H. M., W. D. Yu, X. Z. Yuan, Z. Y. Guo, and X. W. Tian. "Performance Evaluation of Amplitude-Phase Algorithm for SAR Raw Data Compression." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.207.

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