Synthetic Aperture Radar Image Processing Based on Piecewise Linear Mapping

Block adaptive quantization (BAQ) has been an optimum method of SAR raw data compression technique for quite long time due to its simplicity in implementation and results but its performance deteriorates as the saturation level of the data input increased. In order to overcome this drawback, the author has studied the mapping between the average signal magnitude (ASM) and the standard deviation of the input signal (SDIS). We also evaluated 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. Results obtained from simulated data and real data show that the performance of new algorithm is better than conventional BAQ when raw data is having heavy SD.