DocumentCode :
35178
Title :
Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets
Author :
Palsson, Frosti ; Sveinsson, Johannes R. ; Ulfarsson, Magnus Orn ; Benediktsson, Jon Atli
Author_Institution :
Fac. of Electr. & Comput. Eng., Univ. of Iceland, Reykjavik, Iceland
Volume :
53
Issue :
5
fYear :
2015
fDate :
May-15
Firstpage :
2652
Lastpage :
2663
Abstract :
In remote sensing, due to cost and complexity issues, multispectral (MS) and hyperspectral (HS) sensors have significantly lower spatial resolution than panchromatic (PAN) images. Recently, the problem of fusing coregistered MS and HS images has gained some attention. In this paper, we propose a novel method for fusion of MS/HS and PAN images and of MS and HS images. MS and, more so, HS images contain spectral redundancy, which makes the dimensionality reduction of the data via principal component (PC) analysis very effective. The fusion is performed in the lower dimensional PC subspace; thus, we only need to estimate the first few PCs, instead of every spectral reflectance band, and without compromising the spectral and spatial quality. The benefits of the approach are substantially lower computational requirements and very high tolerance to noise in the observed data. Examples are presented using WorldView 2 data and a simulated data set based on a real HS image, with and without added noise.
Keywords :
data reduction; geophysical image processing; hyperspectral imaging; image fusion; principal component analysis; remote sensing; spectral analysis; wavelet transforms; PAN image fusion; PCA; WorldView 2 data; coregistered HS image fusion; coregistered MS image fusion; dimensionality reduction; hyperspectral image sensor; model-based fusion; multispectral image sensor; principal component analysis; remote sensing; spatial quality; spectral quality; spectral redundancy; spectral reflectance band; wavelets; Hyperspectral sensors; Loading; Noise; Principal component analysis; Spatial resolution; Wavelet transforms; Image fusion; maximum a posteriori probability (MAP); principal component analysis (PCA); wavelets;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2014.2363477
Filename :
6951484
Link To Document :
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