A Robust Image Fusion Method Based on Local Spectral and Spatial Correlation

Wang, Huixian; Jiang, Wanshou; Lei, Chuan; Qin, Shanlan; Wang, Jiaolong

doi:10.1109/lgrs.2013.2265915

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…Local estimation of the injection coefficients performs better than global ones by considering the correlations of the neighborhood pixels. However, the local estimation areas for the injection procedure are always restricted in a fixed square window [ 28 ], which will provide frustrating results as the foreground and the background pixels appear in the same square. The mixed situation in a given square window is especially common in high-resolution remote sensing images, since the tiny objects are more likely available for the high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

An Improved Pulse-Coupled Neural Network Model for Pansharpening

Yan

Xie

et al. 2020

Sensors

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Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

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Section: Introductionmentioning

confidence: 99%

An Improved Pulse-Coupled Neural Network Model for Pansharpening

Yan

Xie

et al. 2020

Sensors

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“…In addition, due to the high computational costs of moving window-based algorithms, methods that employ segment-and non-overlapping block-based processing have been proposed [38,39].…”

Section: Overview and Characteristics Of Cs-based Pansharpening Methodsmentioning

confidence: 99%

A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs

et al. 2017

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Abstract:The objective of this work is to develop an algorithm for pansharpening of very high resolution (VHR) satellite imagery that reduces the spectral distortion of the pansharpened images and enhances their spatial clarity with minimal computational costs. In order to minimize the spectral distortion and computational costs, the global injection gain is transformed to the local injection gains using the normalized difference vegetation index (NDVI), on the assumption that the NDVI are positively or negatively correlated with local injection gains obtained from each band of the satellite data. In addition, the local injection gains are then applied in the hybrid pansharpening algorithm to optimize the spatial clarity. In particular, in the proposed algorithm, a synthetic intensity image is determined using block-based linear regression. In experiments using imagery collected by various satellites, such as KOrea Multi-Purpose SATellite-3 (KOMPSAT-3), KOMPSAT-3A and WorldView-3, the pansharpened results obtained using the proposed Hybrid Pansharpening algorithm using NDVI and based on the spectral mode (HP-NDVI spectral ) provide a better representation of the values of the Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), the spectral angle mapper (SAM) and the Q4/Q8 than those produced by existing pansharpening algorithms. In terms of spatial quality, the pansharpened images obtained using the proposed pansharpening algorithm based on the spatial mode (HP-NDVI spatial ) have higher average gradient (AG) values than those obtained using existing pansharpening methods. In addition, the computational complexity of our method is similar to that of a pansharpening algorithm that is based on a global injection model, although our methodology has characteristics that are similar to those of a local injection gain-based model that has a very high computational cost. Thus, the quantitative and qualitative assessments presented here indicate that the proposed algorithm can be utilized in various applications that employ spectral information or require high spatial clarity.

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“…However, restricting this assumption in a spatial homogenous area is always a better option. To this aim, some authors have improved CS methods by restricting this assumption in a local sliding window [44][45][46], in a group of homogenous pixels after image classification [47,48], or paying attention to the mixed pixels [49].…”

Section: The Usefulness Of the Revealed Statistical Assumptionsmentioning

confidence: 99%

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

2017

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The component substitution (CS) pansharpening methods have been developed for almost three decades and have become better understood recently by generalizing them into one framework. However, few studies focus on the statistical assumptions implicit in the CS methods. This paper reveals their implicit statistical assumptions from a Bayesian data fusion framework and suggests best practices for histogram matching of the panchromatic image to the intensity image, a weighted summation of the multispectral images, to better satisfy these assumptions. The purpose of histogram matching was found to make the difference between the high-resolution panchromatic and intensity images as small as possible, as one implicit assumption claims their negligible difference. The statistical relationship between the high-resolution panchromatic and intensity images and the relationship between their corresponding low-resolution images are the same, as long as the low resolution panchromatic image is derived by considering the modulation transfer functions of the multispectral sensors. Hence, the histogram-matching equation should be derived from the low-resolution panchromatic and intensity images, but not derived from the high-resolution panchromatic and expanded low-resolution intensity images. Experiments using three example CS methods, each using the two different histogram-matching equations, was conducted on the four-band QuickBird and eight-band WorldView-2 top-of-atmosphere reflectance data. The results verified the best practices and showed that the histogram-matching equation derived from the high-resolution panchromatic and expanded low-resolution intensity images provides more-blurred histogram-matched panchromatic image and, hence less-sharpened pansharpened images than that derived from the low-resolution image pair. The usefulness of the assumptions revealed in this study for method developers is discussed. For example, the CS methods can be improved by satisfying the assumptions better, e.g., classifying the images into homogenous areas before pansharpening, and by changing the assumptions to be more general to address their deficiencies.

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A Robust Image Fusion Method Based on Local Spectral and Spatial Correlation

Cited by 14 publications

References 17 publications

An Improved Pulse-Coupled Neural Network Model for Pansharpening

An Improved Pulse-Coupled Neural Network Model for Pansharpening

A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

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