CVAug 13, 2021

Full-resolution quality assessment for pansharpening

arXiv:2108.06144v345 citations
Originality Synthesis-oriented
AI Analysis

This addresses the need for reliable evaluation in remote sensing image fusion, but it is incremental as it builds on existing no-reference approaches.

The paper tackles the problem of quality assessment for pansharpening methods by proposing a no-reference full-resolution framework, introducing a reprojection protocol for spectral consistency and a new spatial consistency index, with experimental results demonstrating its effectiveness across different datasets and sensors.

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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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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