A SAR raw data compressor using frequency domain entropy-constrained block adaptive quantization

2005 ◽  
Author(s):  
Theo Algra
Keyword(s):  
Frequency Domain ◽  
Raw Data ◽  
Adaptive Quantization

Study on an Algorithm for SAR Raw Data Compression

2013 ◽  
Vol 380-384 ◽  
pp. 1495-1498
Author(s):  
Shang Chun Zeng ◽  
Yun Xia Xie ◽  
Yi Xian Chen ◽  
Zhao Da Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Data Compression ◽  
Linear Prediction ◽  
Synthetic Aperture ◽  
Bit Rate ◽  
Compression Method ◽  
Raw Data ◽  
Adaptive Quantization ◽  
Difference Series ◽  
Aperture Radar

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.

scholarly journals A Phase-Preserving Focusing Technique for TOPS Mode SAR Raw Data Based on Conventional Processing Methods

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3321
Author(s):  
Adele Fusco ◽  
Antonio Pepe ◽  
Paolo Berardino ◽  
Claudio De Luca ◽  
Sabatino Buonanno ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Computing Time ◽  
Process Data ◽  
Raw Data ◽  
Remarkable Feature ◽  
Radar Sensors ◽  
Antenna Steering ◽  
Progressive Scan ◽  
Processing Techniques ◽  
Frequency Variations

We present a new solution for the phase-preserving focusing of synthetic aperture radar (SAR) raw data acquired through the Terrain Observation with Progressive Scan (TOPS) mode. The proposed algorithm consists of a first interpolation stage of the TOPS raw data, which takes into account the Doppler Centroid frequency variations due to the azimuth antenna steering function, and allows us to unfold the azimuth spectra of the TOPS raw data. Subsequently, the interpolated signals are processed by using conventional phase-preserving SAR focusing methods that exploit frequency domain and spectral analyses algorithms, which are extensively used to efficiently process Stripmap and ScanSAR data. Accordingly, the developed focusing approach is easy to implement. In particular, the presented focusing approach exploits one of the available frequency domain Stripmap processing techniques. The only modification is represented by the inclusion, within the 2D frequency domain focusing step, of a spurious azimuth chirp signal with a properly selected azimuthal rate. This allows us to efficiently carry out the TOPS azimuth focusing through the SPECAN method. Furthermore, an important aspect of this algorithm is the possibility to easily achieve a constant and tunable output azimuth pixel size without any additional computing time; this is a remarkable feature with respect to the full-aperture TOPS-mode algorithms available in the existing literature. Moreover, although tailored on Sentinel-1 (S1) raw data, the proposed algorithm can be easily extended to process data collected through the TOPS mode by different radar sensors. The presented experimental results have been obtained by processing real Sentinel-1 raw data and confirm the effectiveness of the proposed algorithm.

A Compression Algorithm for SAR Data after Range Focusing

2013 ◽  
Vol 694-697 ◽  
pp. 2877-2880
Author(s):  
Shang Chun Zeng ◽  
Xian Lin Deng ◽  
Yi Xian Chen ◽  
Yun Xia Xie ◽  
Zhao Da Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Linear Prediction ◽  
Compression Algorithm ◽  
Synthetic Aperture ◽  
Bit Rate ◽  
Compression Method ◽  
Raw Data ◽  
Adaptive Quantization ◽  
Difference Series ◽  
Sar Data

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.

scholarly journals An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4221
Author(s):  
Wei Ji ◽  
Xiaolan Qiu ◽  
Xuejiao Wen ◽  
Lijia Huang
Keyword(s):  
Power Loss ◽  
Signal To Noise Ratio ◽  
Practical Application ◽  
Scattering Mechanism ◽  
Echo Signal ◽  
Signal To Noise ◽  
Raw Data ◽  
Look Up Table ◽  
Adaptive Quantization ◽  
Simulation Results

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

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