A Blind Full-Resolution Quality Evaluation Method for Pansharpening

Meng, Xiangchao; Bao, Kedi; Shu, Jinfang; Zhou, Bingzhong; Shao, Feng; Sun, Weiwei; Li, Shutao

doi:10.1109/tgrs.2021.3087708

Cited by 16 publications

(10 citation statements)

References 48 publications

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“…Moreover, the MVG-based QNR method (MQNR) extracts multiple spatial and spectral features from the fused images to build the distorted MVG model. The distance between the benchmark MVG model and the distorted MVG model is used to indicate the evaluation results [48]. With the help of large-scale datasets, deep-learning-based NR methods have also emerged and been used for PAN-MS fusion quality assessment [49].…”

Section: Nr Quality Assessmentmentioning

confidence: 99%

“…The typical NR metrics include Quality with No Reference (QNR) [43], filtered QNR (FQNR) [44], regression-based QNR (RQNR) [45], hybrid QNR (HQNR) [46], generalized QNR (GQNR) [47], etc. Moreover, some researchers proposed NR metrics based on a multivariate Gaussian distribution (MVG) model from the perspective of statistical characteristics [48]. With the development of deep learning, some novel evaluation methods based on a deep learning network have also been proposed [49].…”

Section: Introductionmentioning

confidence: 99%

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A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

Hao,

Li,

et al. 2024

Remote Sensing

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In recent years, hyperspectral (HS) sharpening technology has received high attention and HS sharpened images have been widely applied. However, the quality assessment of HS sharpened images has not been well addressed and is still limited to the use of full-reference quality evaluation. In this paper, a novel no-reference quality assessment method based on Benford’s law for HS sharpened images is proposed. Without a reference image, the proposed method detects fusion distortion by performing first digit distribution on three quality perception features in HS sharpened images, using the standard Benford’s law as a benchmark. The experiment evaluates 10 HS fusion methods on three HS datasets and selects four full-reference metrics and four no-reference metrics to compare with the proposed method. The experimental results demonstrate the superior performance of the proposed method.

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Section: Nr Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

Hao,

Li,

et al. 2024

Remote Sensing

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“…There are several other extensions with modifications in the similarity measure [36] and use of the natural image quality evaluator [37]. The spatial and spectral distortions have also been jointly measured using a multivariate Gaussian model [38].…”

Section: B Quality Assessment Of Pansharpened Imagesmentioning

confidence: 99%

No Reference Pansharpened Image Quality Assessment Through Deep Feature Similarity

Badal

Soundararajan

Garg

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Furthermore, optimizing parameters for a given scale may lead to a sort of "scale overfitting", with the perverse effect of degrading performance on a different scale. For these reasons, many recent studies focus on no-reference full-resolution quality indexes [76,78,80,82]. It is also worth recalling that the assessment of spectral and spatial consistencies is not necessarily linked to a fusion task and is of broader interest, with applications in the medical [84], agricultural [85], and food [86] sciences, to mention a few.…”

Section: Reduced-resolution Assessmentmentioning

confidence: 99%

“…Finally, often, the spectral and the spatial consistency indexes are somehow combined, e.g., through geometric means, to provide a single hybrid consistency index representing the overall quality no-reference assessment of the fused images [72][73][74][75][78][79][80][81][82][83].…”

Section: Introductionmentioning

confidence: 99%

Full-Resolution Quality Assessment for Pansharpening

Scarpa

Ciotola

2022

Remote Sensing

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A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground truths to be used as guidance for an objective evaluation has pushed the community to resort to two approaches, which can also be jointly applied. Hence, two kinds of indexes can be found in the literature: (i) reference-based reduced-resolution indexes aimed to assess the synthesis ability; (ii) no-reference subjective quality indexes for full-resolution datasets aimed to assess spectral and spatial consistency. Both reference-based and no-reference indexes present critical shortcomings, which motivate the community to explore new solutions. In this work, we propose an alternative no-reference full-resolution assessment framework. On one side, we introduce a protocol, namely the reprojection protocol, to take care of the spectral consistency issue. On the other side, a new index of the spatial consistency between the pansharpened image and the panchromatic band at full resolution is also proposed. Experimental results carried out on different datasets/sensors demonstrate the effectiveness of the proposed approach.

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A Blind Full-Resolution Quality Evaluation Method for Pansharpening

Cited by 16 publications

References 48 publications

A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

A No-Reference Quality Assessment Method for Hyperspectral Sharpened Images via Benford’s Law

No Reference Pansharpened Image Quality Assessment Through Deep Feature Similarity

Full-Resolution Quality Assessment for Pansharpening

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