.png){kind=icon}
返回
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}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Dictionary learning
image compression
sparse representation
synthetic aperture radar (SAR)
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
.png){kind=icon}
分享收藏Porous Lithiophilic Li–Si Alloy‐Type Interfacial Framework via Self‐Discharge Mechanism for Stable Lithium Metal Anode with Superior Rate (Adv. Energy Mater. 37/2021)通过自放电机理的多孔亲锂锂-Si合金型界面框架，用于稳定的锂金属阳极，具有优越的速率 (Adv. Energy Mater. 37/2021)
OAAI
OAAI分享收藏
查看更多内容