METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS
METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS
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发表时间：
2008
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通讯作者：
Yun Zhang;Wg Vii
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作者：
Yun Zhang;Wg Vii
本文重点对七种常用的图像融合质量评价方法进行了评价和分析，以了解它们是否能够提供令人信服的图像质量或相似性度量。这七个指标是平均偏差（MB）、方差差（VD）、标准差差（SDD）、相关系数（CC）、光谱角映射器（SAM）、相对无干扰全局误差（ERGAS）和Q4质量指数（Q4），它们也被用于IEEE GRSS 2006数据融合竞赛。生成四幅测试图像来评估指标。视觉比较和数字分类表明，四个测试图像具有相同的质量遥感应用，然而，七种评价方法提供不同的测量表明，四个图像有不同的质量。Alparone等人（2004）的图像融合质量评价和IEEE GRSS 2006数据融合竞赛（Alparone等人，2007年，也进行了分析。在两次评价中发现定量测量、视觉比较和最终排名之间存在显著差异。上述三个案例中目视评价与定量分析的不一致性表明，7个定量指标不能为遥感图像质量评价提供可靠的度量。
This paper focuses on the evaluation and analysis of seven frequently used image fusion quality assessment methods to see whether, or not, they can provide convincing image quality or similarity measurements. The seven indexes are Mean Bias (MB), Variance Difference (VD), Standard Deviation Difference (SDD), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), Relative Dimensionless Global Error (ERGAS), and Q4 Quality Index (Q4), which were also used in the IEEE GRSS 2006 Data Fusion Contest. Four testing images are generated to evaluate the indexes. Visual comparison and digital classification demonstrate that the four testing images have the same quality for remote sensing applications; however, the seven evaluation methods provide different measurements indicating that the four images have varying qualities. The image fusion quality evaluation by Alparone, et al. (2004) and that by the IEEE GRSS 2006 data fusion contest (Alparone, et al., 2007) are also analyzed. Significant discrepancy between the quantitative measurements, visual comparison and final ranking has been found in both evaluations. The inconsistency between the visual evaluations and quantitative analyses in the above three cases demonstrate that the seven quantitative indicators cannot provide reliable measurements for quality assessment of remote sensing images.