Title :
A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors
Author :
Xiyan He ; Condat, L. ; Bioucas-Dias, Jose M. ; Chanussot, Jocelyn ; Junshi Xia
Author_Institution :
GIPSA-Lab., Univ. of Grenoble, Grenoble, France
Abstract :
The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: 1) a low spatial resolution multispectral image and 2) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain, we encourage low-rank structure, whereas in the spatial domain, we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and fused multispectral images. A weighted version of the vector total variation norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low-resolution multispectral images by linear regression while the second one employs the principal component pursuit to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed split augmented Lagrangia- shrinkage algorithm to effectively solve the proposed variational formulations. Experimental results on simulated and real remote sensing images show the effectiveness of the proposed pansharpening method compared with the state-of-the-art.
Keywords :
geophysical image processing; image fusion; image resolution; principal component analysis; regression analysis; remote sensing; data matrix; edge location preservation; fused multispectral images; high quality images; high spatial resolution multispectral image; high spatial resolution panchromatic image; image fusion techniques; linear regression; low spatial resolution multispectral image; low-rank structure; multiple sensors; multisensor systems; pansharpening method; principal component pursuit; remote sensing data; remote sensing images; spatial sparsity priors; spectral resolution characteristics; spectral sparsity priors; split augmented Lagrangian shrinkage algorithm; Linear regression; Optimization; Remote sensing; Spatial resolution; TV; Vectors; Image fusion; convex optimization; low rank recovery; pansharpening; principal component pursuit; proximal splitting method; remote sensing; split augmented Lagrangian shrinkage (SALSA); total variation;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2014.2333661