Full-Resolution Quality Assessment for Pansharpening

被引:24
|
作者
Scarpa, Giuseppe [1 ]
Ciotola, Matteo [1 ]
机构
[1] Univ Federico II, Dept Elect Engn & Informat Technol DIETI, I-80125 Naples, Italy
关键词
super-resolution; image enhancement; convolutional neural networks; data fusion; multispectral images; PAN-SHARPENING METHOD; IMAGE FUSION; MULTISPECTRAL IMAGES; SPARSE REPRESENTATION; WAVELET TRANSFORM; SCALE ASSESSMENT; ENHANCEMENT; REGRESSION; CONTRAST; MODEL;
D O I
10.3390/rs14081808
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:25
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