Progressive space frequency quantization for SAR data compression

The authors propose a new wavelet image coding technique for synthetic aperture radar (SAR) data compression called a progressive space-frequency quantization (PSFQ). PSFQ performs spatial quantization via rate distortion-optimized zerotree pruning of wavelet coefficients that are coded using a progressive subband coding technique. They compared the performances of zerotree-based methods: EZW, SPIHT, SFQ, and PSFQ with the classical wavelet-based method (CWM), which uses uniform scalar quantization of subbands followed by recency rank coding. The performances of the methods based on zerotree quantization were better than the CWM in the rate distortion sense. The embedded coding techniques perform better SNR results than the methods using scalar quantization. However, the probability density function (PDF) of the reconstructed amplitude SAR data compressed using CWM, better corresponded to the PDF of the original data than the PDF of the reconstructed data compressed using the zerotree based methods. The amplitude PDF of the reconstructed data obtained using PSFQ compression algorithm better corresponded to the original PDF than the amplitude PDF of the data obtained using the multilook method.

I. Introduction

Synthetic aperture radar (SAR) is a remote sensing technology that uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture in order to yield high spatial resolution radar images. It is an active coherent all weather imaging system that operates in the microwave region of the spectrum. This imagery is well suited to the task of remote ground mapping in many applications. In the last few years, high quality images of the Earth produced by SAR systems, carried on a variety of airborne and space borne platforms, have become increasingly available. The primary geophysical quantity determining the SAR data is the complex radar reflectivity of the scene. The SAR data provide measurements of this reflectivity [1]. The local reflectivity at each pixel is represented by a complex number, so the SAR data are known as complex imagery. The noise-like characteristic of these types of images is known as speckle, and it is strongly reflected in the intensity and amplitude parts of the SAR data. The speckle is in fact a scattering phenomenon that occurs because the resolution of the sensor is insufficient to resolve individual scatters within an imaged pixel. In a homogeneous area where the underlying backscattering coefficient is constant, the fluctuation of SAR intensity is only caused by speckle and could be removed using appropriate filters [2]. The real and imaginary parts of the SAR data have resembling Gaussian distribution. Meanwhile, the phase of the complex SAR data has an almost uniform distribution, and the amplitude part of the complex SAR data belongs to the family of gamma distribution [1].