Iterative scheme for MS image pansharpening based on the combination of multi-resolution decompositions

Image pansharpening in the remote-sensing domain may be defined as the technique of extracting high-resolution details from the panchromatic (PAN) image and injecting them into the multispectral (MS) one in a way to preserve the spectral signature and improve the spatial resolution. In this article,...

Full description

Saved in:
Bibliographic Details
Published in:International journal of remote sensing 2016-12, Vol.37 (24), p.6041-6075
Main Authors: Delleji, T., Kallel, A., Ben Hamida, A.
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Decomposition
Mathematical analysis
Remote sensing
Similarity
Transformations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image pansharpening in the remote-sensing domain may be defined as the technique of extracting high-resolution details from the panchromatic (PAN) image and injecting them into the multispectral (MS) one in a way to preserve the spectral signature and improve the spatial resolution. In this article, the authors propose an image fusion framework that tries to derive sharpened MS image such that: (i) when decimated taking into account the imagery system Modulation Transfer Function (MTF), it equals the original MS image; (ii) when decomposed using discrete wavelet transform (DWT), its geometrical details are those of the PAN image weighted by the compatibility PAN/MS. Indeed, MS sharpening is carried out in two steps. First, pre-pansharpened MS image is obtained using inverse DWT taking as approximations those of the upsampled original MS image and as details those of PAN (to reduce spectral distortion, PAN detail injection is performed proportionally to the similarity PAN/MS). Second, to satisfy (i) and to remove the PAN-MS disagreement, an iteration algorithm (alternatively corrects approximations and details) has been proposed. The proposed approach is designed in two versions inspired by the Generalized Laplacian Pyramid (GLP) and the Gram-Schmidt (GS) transformation, respectively. To validate our approach, Pléiades-1A, Geoeye-1, and Landsat Enhanced Thematic Mapper Plus (ETM+) images are tested. The results of qualitative and quantitative scores are presented and discussed. Compared to well-known techniques, our approach shows generally better results, particularly the one based on GLP formalism.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2016.1249303