Effects of SAR image compression on coherent change detection

Abstract

The performance of coherent (and non-coherent) change detection algorithms is evaluated using complex SAR data that have been processed with various data compression approaches; the hope is that it may be possible to achieve higher compression ratios than could be achieved using classical image compression approaches such as BAQ (block adaptive quantization). BAQ compression is typically applied to raw (I,Q) SAR phase-history data, and our studies show that to obtain reasonably good coherent change detection (CCD) performance from a baseline CCD algorithm, BAQ compression requires at least 4-bit quantization for each of the I and Q phase-history data samples; since our original full-precision data is 8-bits I and 8-bits Q, the best compression ratio (CR) that could be achieved using BAQ compression was a factor of 2. Our goal is to increase the amount of compression while achieving the same quality of change detection using more sophisticated wavelet-based approaches such as compressive sensing or set partitioning (SPIHT). This paper demonstrates a wavelet-based compressive sensing approach that gives CR = 3 with comparable CCD performance; we also demonstrate a wavelet-based SPIHT approach that gives CR = 4 with comparable CCD performance.