Full-Scale Assessment of Pansharpening Through Polynomial Fitting of Multiscale Measurements

Carla, Roberto; Santurri, Leonardo; Aiazzi, Bruno; Baronti, Stefano

doi:10.1109/tgrs.2015.2436699

Cited by 41 publications

(21 citation statements)

References 33 publications

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“…Quality evaluations have been carried out at the full spatial scale (0.50 m for GeoEye-1 products) equal to that of the original Pan [51,52]. The check at full scale foresees separate measurement of spectral consistency, which may be defined according to Wald's protocol [53], and spatial consistency, which may be defined according to either QNR [54] or Khan's [55] protocol.…”

Section: Assessmentsmentioning

confidence: 99%

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: Assessmentsmentioning

confidence: 99%

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

et al. 2018

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“…Despite of its accuracy (thanks to the presence of a reference image), the above-mentioned procedure is based on an implicit assumption of "invariance among scale" that could not be valid. For this reason, several protocols at full resolution, i.e., validating the fused products coming from the fusion of the original MS and PAN images, have been proposed in the literature [52][53][54][55][56][57]. In this work, the hybrid quality with no reference index (HQNR) [58] is selected.…”

Section: Assessment Protocols and Indicesmentioning

confidence: 99%

Fast Reproducible Pansharpening Based on Instrument and Acquisition Modeling: AWLP Revisited

et al. 2019

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Pansharpening is the process of merging the spectral resolution of a multi-band remote-sensing image with the spatial resolution of a co-registered single-band panchromatic observation of the same scene. Conceived and contextualized over 30 years ago, panharpening methods have progressively become more and more sophisticated, but simultaneously they have started producing fewer and fewer reproducible results. Their recent proliferation is most likely due to the lack of standardized assessment procedures and especially to the use of non-reproducible results for benchmarking. In this paper, we focus on the reproducibility of results and propose a modified version of the popular additive wavelet luminance proportional (AWLP) method, which exhibits all the features necessary to become the ideal benchmark for pansharpening: high performance, fast algorithm, absence of any manual optimization, reproducible results for any dataset and landscape, thanks to: (i) spatial analysis filter matching the modulation transfer function (MTF) of the instrument; (ii) spectral transformation implicitly accounting for the spectral responsivity functions (SRF) of the multispectral scanner; (iii) multiplicative detail-injection model with correction of the path-radiance term introduced by the atmosphere. The revisited AWLP has been comparatively evaluated with some of the high performing methods in the literature, on three different datasets from different instruments, with both full-scale and reduced-scale assessments, and achieves the first place, on average, in the ranking of methods providing reproducible results.

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“…QNR D  indicates that the inter-relation between the fused bands should be as identical as the relation between MS bands and QNR s D indicates that the relation between each fused band and PAN image should be identical to those between MS band and low resolution PAN. By combining these two distortions a unique quality index named QNR can be obtained (Alparone et al 2008), (Carlà et al 2015).…”

Section: Quality With No Reference (Qnr) Protocolmentioning

confidence: 99%

“…The foundation of this protocol is based on Wald's consistency property. This protocol uses the basis of Zhou's protocol (extracting high-pass information) to measure the spatial distortion and basis of QNR protocol to measure spectral distortion (Carlà et al 2015). In fact, Khan et al tried to improve the previous protocols by using the strengths of each of the aforementioned protocols.…”

Section: Khan's Protocolmentioning

confidence: 99%

A Critical Review of Quality Assessment Protocols in Pan-Sharpening

Maleki

Ghassemian

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Although vast amounts of pan-sharpening methods have been proposed to date, there has been relatively little published on the topic of qualitative and quantitative assessment of the pan-sharpened multispectral (MS) data. Since a high resolution reference MS image is not available, qualitative and quantitative assessments of the spatially enhanced MS image are a much more challenging task in pan-sharpening. Thomas et al. conducted a critical survey of conventional pan-sharpening methods considering remote sensing physics. In this paper, we study the effects of physical constraints of satellite sensors on conventional quality assessment protocols. The novelty of this work is in investigating the effect of physical constraints on the performance of quality assessment protocols. The most popular protocols, which are analysed here, are quality not requiring a reference (QNR), Wald’s protocol, Zhou’s protocol, Khan’s protocol and Ghassemi’s protocol. In doing so, the strengths and weaknesses of such protocols regarding the physical limitations on satellite sensors are discussed.

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Full-Scale Assessment of Pansharpening Through Polynomial Fitting of Multiscale Measurements

Cited by 41 publications

References 33 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

Fast Reproducible Pansharpening Based on Instrument and Acquisition Modeling: AWLP Revisited

A Critical Review of Quality Assessment Protocols in Pan-Sharpening

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