Compression of raw SAR data using entropy-constrained quantization

Algra, T.

doi:10.1109/igarss.2000.859673

Cited by 19 publications

(12 citation statements)

References 5 publications

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“…ECBAQ has been described in [2] and [3]. Recently in [4] a new version of ECBAQ has been presented using a rate control loop which is optimized for frequency domain applications.…”

Section: Introductionmentioning

confidence: 99%

Advanced on-board SAR Data Compressor

Algra

Bierens²

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…ECBAQ has been described in [2] and [3]. Recently in [4] a new version of ECBAQ has been presented using a rate control loop which is optimized for frequency domain applications.…”

Section: Introductionmentioning

confidence: 99%

Advanced on-board SAR Data Compressor

Algra

Bierens²

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…Many efforts were made to develop suitable compression techniques of the bit stream necessary for raw data coding [1][2][3][4][5][6], such as adaptive scalar [7][8][9] and vector quantization [10][11][12] techniques. Considering that the real and imaginary parts of the complex received signal are Gaussian-distributed, with zero mean and same variance, BAQ uses a scalar quantizer controlled by the statistics of the raw SAR data to quantize these data with fewer bits.…”

Section: Introductionmentioning

confidence: 99%

Raw SAR data compression by structurally random matrix based compressive sampling

Wang

2009

2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar

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Synthetic aperture radar (SAR) is an imaging system which can provide high resolution images of earth surface. It transmits chirp signals and the received echoes are sampled into I and Q components, thus producing a huge amount of raw SAR data which may exceed the on-board storage and downlink bandwidth. In this paper, we compress the raw SAR data by sampling the signal below the well-known Nyquist rate using a novel framework of compressive sampling (CS), i.e, a fast and efficient sampling with structurally random matrices(SRM) which is developed on the provable mathematical model. In this framework, a 2DFFT and a structurally random matrix whose columns are permuted randomly are employed in the encoder. At the decoder the basis pursuit reconstruction then proceeds to find the sparsest signal. Simulation results are also presented to prove the feasibility of our proposed scheme.

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“…2 (solid line), where it is compared with the curves obtained with an entropy constrained uniform quantizer (dotted line) (Algra, 2000). Fig.…”

Section: Dr Drtlkmentioning

confidence: 99%

“…A possible choice of the basis is the overcomplete Independent Component Analysis (ICA) basis (Hyvarinen et al, 1999, Algra, 2000, allowing to model the data as a mixture of non Gaussian and "almost" statistically independent sources, so that the representation coefficients, due to their scarce correlation, can be efficiently coded using a scalar quantizer.…”

Section: Introductionmentioning

confidence: 99%