mlsanityData to 7 Oct 2026

Full-resolution quality assessment for pansharpening

arXiv 2108.06144 ↗First posted 13 Aug 2021cs.CVcited by 8

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

  1. 2017Mask R-CNNcited by 7,259
  2. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  3. 2018Object Detection with Deep Learning: A Reviewcited by 524
  4. 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 65
  5. 2017A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpeningcited by 54
  6. 2018PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpeningcited by 24
  7. 2017Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual networkcited by 23
  8. 2017Target-adaptive CNN-based pansharpeningcited by 20
  9. 2018CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondencescited by 15

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 3.

8 papers.