Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

Garzelli, Andrea; Nencini, F.

doi:10.1016/j.patcog.2007.05.002

Cited by 47 publications

(25 citation statements)

References 15 publications

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“…In applications concerning different acquisition dates, e.g., change detection [46] and multitemporal pansharpening [47,48], corrections for Sun elevation and atmospheric effects, both reductive and diffusive, should be performed according to (15). Another typical case is the calculation of the normalized differential vegetation index (NDVI) from multispectral images.…”

Section: Data Formats and Productsmentioning

confidence: 99%

“…If the available data are in spectral radiance format (15), the first correction is the subtraction of path-radiance from the measured spectral radiance values, or de-hazing. The subsequent correction for the total irradiance and upward transmittance is less crucial, given the fractional nature of NDVI and the fact that spectrally-adjacent bands will have similar irradiances and transmittances.…”

Section: Data Formats and Productsmentioning

confidence: 99%

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Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

et al. 2018

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Whenever vegetated areas are monitored over time, phenological changes in land cover should be decoupled from changes in acquisition conditions, like atmospheric components, Sun and satellite heights and imaging instrument. This especially holds when the multispectral (MS) bands are sharpened for spatial resolution enhancement by means of a panchromatic (Pan) image of higher resolution, a process referred to as pansharpening. In this paper, we provide evidence that pansharpening of visible/near-infrared (VNIR) bands takes advantage of a correction of the path radiance term introduced by the atmosphere, during the fusion process. This holds whenever the fusion mechanism emulates the radiative transfer model ruling the acquisition of the Earth's surface from space, that is for methods exploiting a multiplicative, or contrast-based, injection model of spatial details extracted from the panchromatic (Pan) image into the interpolated multispectral (MS) bands. The path radiance should be estimated and subtracted from each band before the product by Pan is accomplished. Both empirical and model-based estimation techniques of MS path radiances are compared within the framework of optimized algorithms. Simulations carried out on two GeoEye-1 observations of the same agricultural landscape on different dates highlight that the de-hazing of MS before fusion is beneficial to an accurate detection of seasonal changes in the scene, as measured by the normalized differential vegetation index (NDVI).

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Section: Data Formats and Productsmentioning

confidence: 99%

Section: Data Formats and Productsmentioning

confidence: 99%

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

et al. 2018

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“…How the accuracy of nominal MTF would influence quality assessment should be studied, which might lead to the question of how to validate pan-sharpening when MTF is unavailable or inaccurate. Techniques used by adaptive methods mentioned in the letter, along with others handling information among scales such as SIFT [23] and Kalman Filter [24,25], might help in the study. Consistency property measurement is another crucial problem concerning spatial degradation.…”

Section: Discussionmentioning

confidence: 99%

Image Degradation for Quality Assessment of Pan-Sharpening Methods

Dou

2018

Remote Sensing

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Wald's protocol is the most widely accepted protocol to assess pan-sharpening algorithms. In particular, the synthesis property-which is usually validated on a reduced scale-is thought to be a necessary and sufficient condition of a success image fusion. Usually, the synthesis property is evaluated at a reduced resolution scale to take the original multispectral (MS) image as reference; thus, the image degradation method that is employed to produce reduced resolution images is crucial. In the past decade, the standard method has been to decimate the low-pass-filtered image where the filter is designed to match the modulation transfer function (MTF) of the sensor. The paper pointed out the deficiency of the method, and proposed a new image degradation method, referred to as method of spatial degradation for fusion validation (MSD4FV), which takes MTF compensation into account based on a simplified MTF model. The simulation results supported the implicit assumption of Wald's protocol that image fusion performance is invariant among scales if the images have been properly degraded.

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“…Choosing N to be white facilitates the denoising problem Equation (18). However, W colors the noise, so that N becomes colored.…”

Section: Bayesian Approachesmentioning

confidence: 99%

“…However, W colors the noise, so that N becomes colored. Equation (17) and Equation (18) are iteratively solved using the EM algorithm. An estimation of Z is obtained from a restoration of the observation Y combined with a fusion with the observation X.…”

Section: Bayesian Approachesmentioning

confidence: 99%

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

Garzelli

2016

Remote Sensing

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A critical analysis of remote sensing image fusion methods based on the super-resolution (SR) paradigm is presented in this paper. Very recent algorithms have been selected among the pioneering studies adopting a new methodology and the most promising solutions. After introducing the concept of super-resolution and modeling the approach as a constrained optimization problem, different SR solutions for spatio-temporal fusion and pan-sharpening are reviewed and critically discussed. Concerning pan-sharpening, the well-known, simple, yet effective, proportional additive wavelet in the luminance component (AWLP) is adopted as a benchmark to assess the performance of the new SR-based pan-sharpening methods. The widespread quality indexes computed at degraded resolution, with the original multispectral image used as the reference, i.e., SAM (Spectral Angle Mapper) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse), are finally presented. Considering these results, sparse representation and Bayesian approaches seem far from being mature to be adopted in operational pan-sharpening scenarios.

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Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

Cited by 47 publications

References 15 publications

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover

Image Degradation for Quality Assessment of Pan-Sharpening Methods

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

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