A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm

Garzelli, Andrea

doi:10.3390/rs8100797

Cited by 79 publications

(35 citation statements)

References 44 publications

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“…Remotely-sensed images have exhibited explosive growth trends in multi-sensor, multi-temporal, and multi-resolution characteristics. However, there are contradictions between the resolution limitations of current remote sensing systems and the increasing need for high-spatial, high-temporal, and high-spectrum resolutions of satellite images [1][2][3]. One limitation is the spectral and spatial resolution tradeoff, e.g., more than 70% of current optical earth observation satellites simultaneously collect low spatial resolution (LR) multispectral and high spatial resolution (HR) panchromatic images.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

2017

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“…Downscaling using panchromatic bands has been studied for several decades and the detailed critical surveys can be found in [40][41][42]. Such methods are often grouped into component substitution (CS) and multiresolution analysis (MRA) methods [36,41,43,44].…”

Section: Downscaling Landsat-8 30-m Data To 15 M Using the Panchromatmentioning

confidence: 99%

“…It is sometimes termed data fusion, or more specifically, pansharpening, when the higher resolution ancillary data is the panchromatic band [35][36][37][38][39]. Hereafter, for consistency, the term downscaling is used throughout this paper.…”

Section: Downscaling Landsat-8 30-m Data To 15 M Using the Panchromatmentioning

confidence: 99%

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

Zhang

Roy

et al. 2017

Remote Sensing

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Abstract:The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use.

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“…As for regularization methods, the most popular one is the CS-based SAR imaging algorithms, which assume that most imaging scenes are sparse as they often contain little scatter. The CS-based SAR imaging systems can break the Nyquist law and have potential advantages in reducing system sampling rate, improving image quality, reducing measurement burden, increasing the scope of the survey scene, and improving anti-jamming performance [3]. Thus, it becomes a research front in microwave imaging field.…”

Section: Introductionmentioning

confidence: 99%

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

Wang

et al. 2017

Remote Sensing

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Abstract:In recent decades, compressive sensing (CS) is a popular theory for studying the inverse problem, and has been widely used in synthetic aperture radar (SAR) image processing. However, the computation complexity of CS-based methods limits its wide applications in SAR imaging. In this paper, we propose a novel sparse SAR imaging method using the Multiple Measurement Vectors model to reduce the computation cost and enhance the imaging result. Based on using the structure information and the matched filter processing, the new CS-SAR imaging method can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling with the advantages of saving the computational cost substantially both in time and memory. The results of simulations and real SAR data experiments suggest that the proposed method can realize SAR imaging effectively and efficiently.

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A Review of Image Fusion Algorithms Based on the Super-Resolution Paradigm

Cited by 79 publications

References 44 publications

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

Revealing Implicit Assumptions of the Component Substitution Pansharpening Methods

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

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