SAR image compression with the Gabor transform

Abstract

A compression system based on the Gabor transform is applied to detected synthetic aperture radar (SAR) imagery. The Gabor transform is a combined spatial-spectral transform that provides local spatial-frequency analyses in overlapping neighborhoods of the image. Gabor coefficients are efficiently computed using the fast Fourier transform (FFT), and a technique for visualizing the coefficients is demonstrated. Theoretical and practical constraints imposed by the Gabor transform are discussed. The compression system includes bit allocation, quantization, and lossless encoding and decoding stages. Bit allocation tradeoffs are discussed and related to perceptual image quality as well as computational measures of image fidelity. Adaptive scalar, vector, and trellis-coded quantizers are compared. Multifrequency codebooks are designed using ten training images derived from data collected at different aspect angles. Subjective image quality assessment experiments indicate that the Gabor transform/trellis-coded quantizer compression system performs significantly better than adaptive scalar and vector quantizers and JPEG on these SAR images.