Study on SAR Raw Data Compression Techniques
Zhaoda Zhu · Nanjing Hangkong Hangtian Daxue xuebao · 2005
Two raw data compression algorithms are applied to synthetic aperture radar (SAR) raw data. The block adaptive tree-structure vector quantization (BATSVQ) and the block adaptive predictive quantization (BAPQ) are analyzed. To compare the computational load of BATSVQ with full search block adaptive vector quantization (BAVQ), the LBG algorithm is used to generate a code book for full search. The BATSVQ outperforms the BAVQ at the same rate. Because a linear predictor with few taps can capture most of raw signal correlation, the performance of the BAPQ is superior to that of the block adaptive quantization (BAQ). By using the airborne SAR raw data, the performances achieved in terms of bit reduction and certain quality parameters in the image domain have been evaluated. The BATSVQ and BAPQ algorithms seems well-suited to raw SAR data compression in terms of computational complexity and quality of encoded image at low rates.