Paper Info
Title
A Model-Based Method for Pan-Sharpening of Multi-Spectral Images using Sparse Representation
Abstract
Pan-sharpening (PS) is fusion of the low-resolution multi-spectral (LRM) image with the corresponding high resolution panchromatic (HRP) one, which aims to reach the high-resolution multi-spectral (HRM) image. Due to the importance of designing well-adapted dictionaries for the pansharpening problem as the fundamental challenge in the pan-sharpening methods utilizing sparse representation, we present a novel strategy to tackle this issue. Our method takes the image formation model into account in a patch-based strategy and exploits local spectral-spatial information. To make a fair tradeoff between spectral and spatial information of each patch, some local parameters are considered and estimated adaptively. In addition, the energy ratio between LRM/LRP patches is considered to locally reconstruct the same ratio for the corresponding HRM/HRP patches. This model-based pan-sharpening strategy led to a closed-form solution, which results in precise HRM dictionary atoms. To obtain the unknown HRM image, after designing the HRM dictionary from input Pan and multi-spectral images, sparse representation over LRM and HRM dictionaries will be conducted in the sparse coding stage. The proposed method has been applied to two different datasets collected by WorldView-3 and GeoEye-1, and then compared with some popular and state-of-the-art methods, qualitatively and quantitatively.
Year
DOI
Venue
2019
10.1109/ICSIPA45851.2019.8977738
2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
Keywords
Field
DocType
fusion,panchromatic,multi-spectral,pansharpening,sparse-representation,spectral,spatial,dictionary
Spatial analysis,Sharpening,Computer vision,Pattern recognition,Computer science,Panchromatic film,Neural coding,Sparse approximation,Image formation,Artificial intelligence,Multi spectral
Conference
ISSN
ISBN
Citations 
2373-681X
978-1-7281-3378-2
0
PageRank 
References 
Authors
0.34
18
3
Name
Order
Citations
PageRank
Mohammad Khateri100.68
Hassan Ghassemian239634.04
Fardin Mirzapour300.34