Predictive quantization of dechirped spotlight-mode SAR raw data in transform domain

Synthetic aperture radar (SAR) systems collect large volumes of data that must be transmitted to a ground station for storage and processing. However, given the limited bandwidth of the downlink channel it is imperative that SAR data be compressed before transmission. While it is commonly believed that raw SAR data is uncorrelated, it is shown in [1] that the inverse Fourier transform of spotlight-mode SAR exhibits non-negligible correlation that can be exploited in a predictive quantization scheme. In this paper, we propose two predictive quantization algorithms—transform-domain block predictive quantization (TD-BPQ), and transform-domain block predictive vector quantization (TD-BPVQ)—to encode dechirp-on-receive spotlight-mode SAR raw data. Experimental results indicate that, on average, TD-BPQ and TD-BPVQ outperform the well known block adaptive quantization (BAQ) by 5 and 6 dB, respectively.

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

[2]  T. Blumensath,et al.  Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[3]  Mort Naraghi-Pour,et al.  Autoregressive modeling of dechirped spotlight-mode sar rawdata in transform domain , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[4]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[5]  Luisa Verdoliva,et al.  Compression of SAR data through range focusing and variable-rate vector quantization , 2000, IEEE Trans. Geosci. Remote. Sens..

[6]  Enrico Magli,et al.  Lossy predictive coding of SAR raw data , 2003, IEEE Trans. Geosci. Remote. Sens..

[7]  Michel Barlaud,et al.  Raw SAR data compression using vector quantization , 1995 .

[8]  P. Schultheiss,et al.  Block Quantization of Correlated Gaussian Random Variables , 1963 .

[9]  T. Algra.  Compression of raw SAR data using entropy-constrained quantization , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[10]  Michael W. Marcellin,et al.  Compression of synthetic aperture radar video phase history data using trellis-coded quantization techniques , 1999, IEEE Trans. Geosci. Remote. Sens..

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

[12]  Charles W. Therrien,et al.  Discrete Random Signals and Statistical Signal Processing , 1992 .

[13]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[14]  Alberto Moreira,et al.  A comparison of several algorithms for SAR raw data compression , 1995, IEEE Trans. Geosci. Remote. Sens..

[15]  Robert A. Baxter.  SAR image compression with the Gabor transform , 1999, IEEE Trans. Geosci. Remote. Sens..

[16]  J. Bolle,et al.  CODING OF SAR RAW DATA USING REVERSIBLE TRANSFORMS , 1997 .

[17]  Luisa Verdoliva,et al.  Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization , 2001, IEEE Trans. Image Process..

[18]  Vito Pascazio,et al.  SAR raw data compression by subband coding , 2003, IEEE Trans. Geosci. Remote. Sens..

[19]  Salvatore Coco,et al.  A New Methodology for Synthetic Aperture Radar (SAR) Raw Data Compression Based on Wavelet Transform and Neural Networks , 2004, ICAISC.

[20]  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.

[21]  David L. Neuhoff,et al.  Mismatched DPCM encoding of autoregressive processes , 1990, IEEE Trans. Inf. Theory.