IEEE Trans Image Process - A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

Tópicos

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Resumo

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: a) a low spatial resolution multispectral image; and b) 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 the fused multispectral images. A weighted version of the vector Total Variation (TV) 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 the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) 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 Lagrangian Shrinkage (SALSA) 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 to the state-of-the-art.

Resumo Limpo

develop multisensor system recent year led great increas amount avail remot sens data imag fusion techniqu aim infer high qualiti imag given area degrad version area obtain multipl sensor paper focus pansharpen infer high spatial resolut multispectr imag two degrad version complementari spectral spatial resolut characterist low spatial resolut multispectr imag b high spatial resolut panchromat imag introduc new variat model base spatial spectral sparsiti prior fusion spectral domain encourag lowrank structur wherea spatial domain promot sparsiti local differ given fact panchromat multispectr imag integr under continu spectra use differ channel respons propos exploit appropri regular base spatial spectral link panchromat fuse multispectr imag weight version vector total variat tv norm data matrix employ align spatial inform fuse imag panchromat imag regard spectral inform two differ type regular propos promot soft constraint linear depend panchromat fuse multispectr imag first one estim direct linear coeffici observ panchromat low resolut multispectr imag linear regress lr second one employ princip compon pursuit pcp obtain robust recoveri under lowrank structur also show two regular strong relat basic idea regular fuse imag lowrank preserv edg locat use variat recent propos split augment lagrangian shrinkag salsa algorithm effect solv propos variat formul experiment result simul real remot sens imag show effect propos pansharpen method compar stateoftheart

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