A New Methodology for Synthetic Aperture Radar (SAR) Raw Data Compression Based on Wavelet Transform and Neural Networks

Abstract

Synthetic Aperture Radar (SAR) raw data are characterized by a high entropy content. As a result, conventional SAR compression techniques (such as block adaptive quantization and its variants) do not provide fully satisfactory performances. In this paper, a novel methodology for SAR raw data compression is presented, based on discrete wavelet transform (DWT). The correlation between the DWT coefficients of a SAR image at different resolutions is exploited to predict each coefficient in a subband mainly from the (spatially) corresponding ones in the immediately lower resolution subbands. Prediction is carried out by classical multi-layer perceptron (MLP) neural networks, all of which share the same, quite simple topology. Experiments carried out show that the proposed approach provides noticeably better results than most state-of-the-art SAR compression techniques.