Low-rank sparse matrix decomposition for sparsity-driven SAR image reconstruction

Soğanlı, Abdurrahim and Çetin, Müjdat (2015) Low-rank sparse matrix decomposition for sparsity-driven SAR image reconstruction. In: 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, Italy (Accepted/In Press)

WarningThere is a more recent version of this item available.

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


We consider the development of a synthetic aperture radar (SAR) image reconstruction method that decomposes the imaged field into a sparse and a low-rank component. Such a decomposition is of interest in image analysis tasks such as segmentation and background subtraction. Conventionally, such operations are performed after SAR image formation. However image formation methods may produce images that are not well suited for such interpretation tasks since they do not incorporate interpretation objectives to the SAR imaging problem. We exploit recent work on sparse and low-rank decomposition of matrices and incorporate such a decomposition into the process of SAR image formation. The outcome is a method that jointly reconstructs a SAR image and decomposes the formed image into its low-rank background and spatially sparse components. We demonstrate the effectiveness of the proposed method on both synthetic and real SAR images.

Item Type:Papers in Conference Proceedings
Uncontrolled Keywords:Synthetic aperture radar (SAR), image reconstruction, low-rank sparse matrix decomposition
ID Code:26941
Deposited By:Abdurrahim Soğanlı
Deposited On:22 May 2015 15:42
Last Modified:24 Dec 2015 12:39

Available Versions of this Item

Repository Staff Only: item control page