SAR SUPER RESOLUTION VIA MULTI-DICTIONARY

被引：2
|
作者：
He, Chu [1 ]
Liu, Longzhu [1 ]
Liu, Ming [1 ]
Feng, Qian [1 ]
Liao, Mingsheng [2 ]
机构：
[1] Wuhan Univ, Sch Elect Informat, Wuhan 430079, Peoples R China
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
来源：
2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2011年
关键词：
super-resolution; classification; dictionary; Synthetic Aperture Radar (SAR); sparse representation;
D O I：
10.1109/IGARSS.2011.6048975
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents a novel approach for super-resolution (SR) reconstruction in Synthetic Aperture Radar (SAR), based on multi-dictionary. In comparison with conventional SR via sparse representation, the algorithm combines the classification with sparse representation. After classifying the training image, we jointly train the low and high resolution dictionaries for each class. And then, the image patches are reconstructed according to different dictionaries, which are chosen in conformity with the class of the image patches. The effectiveness of this method is demonstrated on Terra-SAR datasets.
引用
收藏
页码：366 / 369
页数：4
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