A comparison of several algorithms for SAR raw data compression

Proposes new algorithms for synthetic aperture radar (SAR) raw data compression and compares the resulting image quality with the quality achieved by commonly used methods. The compression is carried out in time and frequency domain, with statistic, crisp, and fuzzy methods. The algorithms in the time domain lead to high resolution and a good signal-to-noise ratio, but they do not optimize the performance of the compression according to the frequency envelope of the signal power in both range and azimuth directions. The hardware requirements for the compression methods in the frequency domain are significant, but a higher performance is obtained. Even with a data rate of 3 bits/sample, a satisfactory phase accuracy is achieved which is an essential parameter for polarimetric and interferometric applications. Preliminary analysis concerning the suitability of the proposed algorithms for different SAR applications shows that the compression ratio should be adaptively selected according to the specific application. >

[1]  Ian G. Cumming,et al.  Synthetic aperture radar signal data compression using block adaptive quantization , 1994 .

[2]  U. C. Benz,et al.  A fuzzy block adaptive quantizer (FBAQ) for synthetic aperture radar , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[3]  Alberto Moreira,et al.  Fusion of block adaptive and vector quantizer for efficient SAR data compression , 1993, Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium.

[4]  John C. Curlander,et al.  Synthetic Aperture Radar: Systems and Signal Processing , 1991 .

[5]  Ron Kwok,et al.  Block adaptive quantization of Magellan SAR data , 1989 .

[6]  R. Horn,et al.  E-sar - The Experimental Airborne L/c-band Sar System Of DFVLR , 1988, International Geoscience and Remote Sensing Symposium, 'Remote Sensing: Moving Toward the 21st Century'..

[7]  R. Gray,et al.  Vector quantization , 1984, IEEE ASSP Magazine.

[8]  N. Ahmed,et al.  FAST TRANSFORMS, algorithms, analysis, applications , 1983, Proceedings of the IEEE.

[9]  Gene W. Zeoli,et al.  A lower bound on the date rate for synthetic aperture radar , 1976, IEEE Trans. Inf. Theory.

[10]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[11]  U. Benz,et al.  Optimization of a Fuzzy Block Adaptive Quantizer (FBAQ) for Real Time SAR Raw Data Reduction. , 1995 .

[12]  Th. Eiting Anwendung von Sequenztransformationen auf SAR-Daten zur Datenreduktion. , 1994 .

[13]  Alberto Moreira,et al.  A Block Adaptive Vector Quantization for Optimized SAR Raw Data Reduction. , 1994 .

[14]  Alberto Moreira,et al.  Simulation and Performance Evaluation of the Real-Time Subaperture (RTS) Processor for the E-SAR System of DLR , 1991 .

[15]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[16]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..