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When the saturation degree (SD) of space-borne SAR raw data is high, the per-formance of conventional block adaptive quantization (BAQ) deteriorates obviously. In order to overcome the drawback, this paper studies the mapping between the average signal magnitude (ASM) and the standard deviation of the input signal (SDIS) to the A/D from the original reference. Then, it points out the mistake of the mapping and introduces the concept of the standard deviation of the output signal (SDOS) from the A/D. After that, this paper educes the mapping between the ASM and SDOS from the A/D. Monte-Carlo experiment shows that none of the above two mappings is the optimal in the whole set of SD. Thus, this paper proposes the concept of piecewise linear mapping and the searching algorithm in the whole set of SD. According to the linear part, this paper gives the certification and analytical value of k and for nonlinear part, and utilizes the searching algorithm mentioned above to search the corresponding value of k. Experimental results based on simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is in heavy SD.
When the saturation degree (SD) of space-borne SAR raw data is high, the per-formance of conventional block adaptive quantization (BAQ) deteriorates obviously. This order is overcome the drawback, this paper studies the mapping between the average signal magnitude ASM) and the standard deviation of the input signal (SDIS) to the A / D from the original reference. Then, it points out the mistake of the mapping and introduces the concept of the standard deviation of the output signal (SDOS) from the A / D. After that, this paper educes the mapping between the ASM and SDOS from the A / D. Monte-Carlo experiment shows that none of the above two mappings is the optimal in the whole set of SD. the concept of piecewise linear mapping and the searching algorithm in the whole set of SD. according to the linear part, this paper gives the certification and analytical value of k and for nonlinear part, and utilizes the searching algorithm mentioned above search to correspond ing value of k. Experimental results based on simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is in heavy SD.