Multiscale MAP filtering of SAR images

• Published in: IEEE transactions on image processing 2001-01, Vol.10 (1), p.49-60
• Main Authors: Foucher, S., Benie, G.B., Boucher, J.-M.
• Format: Article
• Language: English
• Subjects: Adaptive filters; Applied sciences; Coefficients; Coherence; Exact sciences and technology; Filtering; Fundamental areas of phenomenology (including applications); Image forming and processing; Image processing; Imaging and optical processing; Information, signal and communications theory; Noise; Optics; Physics; Pixel; Probability density function; Probability density functions; Radar detection; Radar imaging; Reflectivity; Services and terminals of telecommunications; Signal processing; Synthetic aperture radar; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telemetry. Remote supervision. Telewarning. Remote control; Telemetry: remote control, remote sensing; radar; Wavelet; Wavelet coefficients
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.892442

Synthetic aperture radar (SAR) images are disturbed by a multiplicative noise depending on the signal (the ground reflectivity) due to the radar wave coherence. Images have a strong variability from one pixel to another reducing essentially the efficiency of the algorithms of detection and classification. We propose to filter this noise with a multiresolution analysis of the image. The wavelet coefficient of the reflectivity is estimated with a Bayesian model, maximizing the a posteriori probability density function. The different probability density function are modeled with the Pearson system of distributions. The resulting filter combines the classical adaptive approach with wavelet decomposition where the local variance of high-frequency images is used in order to segment and filter wavelet coefficients.