Full-resolution quality assessment for pansharpening
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 9 of its 101 references that are in the map. First posted on arXiv 13 Aug 2021.
Builds on
9 of 101 references.
References · 9
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- 2017A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpeningcited by 54
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- 2017Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual networkcited by 23
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Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 3.
8 papers.
- 2021Pansharpening by convolutional neural networks in the full resolution frameworkcited by 15
- 2023Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelitycited by 8
- 2024Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectivescited by 8
- 2023Band-wise Hyperspectral Image Pansharpening using CNN Model Propagationcited by 5
- 2024FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitionscited by 5
- 2025Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Needcited by 2
- 2025Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Controlcited by 1
- 2025Two-Stage Random Alternation Framework for One-Shot Pansharpeningcited by 0
- 2025Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need25 Oct 2025
- 2025Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control22 May 2025
- 2025Two-Stage Random Alternation Framework for One-Shot Pansharpening10 May 2025
- 2024FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions23 Sep 2024
- 2024Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives1 Jul 2024
- 2023Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation11 Nov 2023
- 2023Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity26 Jul 2023
- 2021Pansharpening by convolutional neural networks in the full resolution framework16 Nov 2021