Model Based SAR Data Compression
Abstract
In this paper a wavelet based method for SAR data denoising and compression is presented. An unsupervised stochastic model based approach to image denoising is presented. SAR image is modeled in wavelet domain Gauss Markov Random field and noise is considered as Gaussian with unknown variance. The parameters are estimated from incomplete data using mixtures of wavelet coefficients, and expectation maximization algorithm. The expectation maximization algorithm is used to efficiently compute a maximum a posteriori estimate. Observed wavelet coefficient is estimated using inter and intra scale of wavelet coefficients to estimate image and noise model parameters. Presented wavelet based method efficiently removes noise from SAR images. The second step is to design an entropy coder that efficiently codes despeckled image. The texture parameters obtained at the despeckling stage are used in the compression process. The image coder is tested on X-SAR data with and achieves comparable compression results with the wavelet based state-of-the art coders for SAR data compression. Keywords- gauss markov random field, wavelet transform, mixture coefficients, compression
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 25, 2005
- Accession Number
- ADA452236
Entities
People
- Dusan Gleich
- Mihai Datcu
- Zarko Cucej
Organizations
- German Aerospace Center