A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
- ECU Author/Contributor (non-ECU co-authors, if there are any, appear on document)
- Dongsheng,Lv,Xiaolei,Wang,Yong,Lin,Xue,Qian,Jiang Fang (Creator)
- Institution
- East Carolina University (ECU )
- Web Site: http://www.ecu.edu/lib/
Abstract: An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes.
Additional Information
- Publication
- Other
- Language: English
- Date: 2016
| Title | Location & Link | Type of Relationship |
| A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage | http://hdl.handle.net/10342/8478 | The described resource references, cites, or otherwise points to the related resource. |