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.