A Practical Compressed Sensing-Based Pan-Sharpening Method

被引：140

作者：

Jiang, Cheng ^{[1
]}

Zhang, Hongyan ^{[1
]}

Shen, Huanfeng ^{[2
]}

Zhang, Liangpei ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China

[2] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2012年 / 9卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Compressed sensing (CS); image fusion; joint dictionary; tradeoff; REMOTELY-SENSED IMAGES; MULTIRESOLUTION FUSION; SPARSE REPRESENTATION; MODEL;

D O I：

10.1109/LGRS.2011.2177063

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

High-resolution multispectral (HRM) images are widely used in many remote sensing applications. Using the pan-sharpening technique, a low-resolution multispectral (LRM) image and a high-resolution panchromatic (HRP) image can be fused to an HRM image. This letter proposes a new compressed sensing (CS)-based pan-sharpening method which views the image observation model as a measurement process in the CS theory and constructs a joint dictionary from LRM and HRP images in which the HRM is sparse. The novel joint dictionary makes the method practical in fusing real remote sensing images, and a tradeoff parameter is added in the image observation model to improve the results. The proposed algorithm is tested on simulated and real IKONOS images, and it results in improved image quality compared to other well-known methods in terms of both objective measurements and visual evaluation.

引用

页码：629 / 633

页数：5

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