.png){kind=icon}
Return
SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2012.2230394.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this letter, we focus on a new compression scheme for synthetic aperture radar (SAR) amplitude images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of radar imaging, SAR images have some distinct properties when compared with natural images that can affect the design of a compression method. First, we introduce SAR properties, sparse representation, and dictionary learning theories. Second, we propose a novel SAR image compression scheme by using multiscale dictionaries. The experimental results carried out on amplitude SAR images reveal that, when compared with JPEG, JPEG2000, and a single-scale dictionary-based compression scheme, the proposed method is better for preserving the important features of SAR images with a competitive compression performance.
Keywords:
Dictionary learning
image compression
sparse representation
synthetic aperture radar (SAR)
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSave
ShareSave
View more