深圳先进技术研究院数字所
siat.cas.cn
20
六月
2017

基于面向特征的四阶相关系数的遥感影像融合质量评估方法

在遥感影像融合领域,为了满足人类视觉系统的需要,空间细节丰富的全色波段和光谱信息丰富的多光谱波段在融合后影像中得到体现。在本文提出了一种基于四阶相关系数(FFOCC)的影像融合评价指标,该指标以特征相似度为基准,其特征选取首次同时涵盖了全色波段中的空间特征和多光谱波段中的光谱特征。利用特征指标的相似度分别衡量空间特征和光谱特征的融合精度。 与此同时,在本文中,FFOCC评价方法于现有常用的评价指标进行了对比,例如Erreur Relative Globale Adimensionnelle de Synthese和无参考质量评估。实验分别对影像融合和影像形变两个方面开展实验,实验结果与主观评估结果保持一致。实验表明FFOCC评价方法对来源于不同融合方法的融合影像,以及由于模糊,添加噪声,改变强度等影像形变处理后的影像,具有更好的表现。实验表明,该方法是一种客观有效的遥感影像融合评价指标。

In remote sensing fusion, the spatial details of a panchromatic (PAN) image and the spectrum information of multispectral (MS) images will be transferred into fused images according to the characteristics of the human visual system. Thus, a remote sensing image fusion quality assessment called feature-based fourth-order correlation coefficient (FFOCC) is proposed.FFOCC is based on the feature-based coefficient concept. Spatial features related to spatial details of the PAN image and spectral features related to the spectrum information of MS images are first extracted from the fused image. Then, the fourth-order correlation coefficient between the spatial and spectral features is calculated and treated as the assessment result. FFOCC was then compared with existing widely used indices, such as Erreur Relative Globale Adimensionnelle de Synthese, and quality assessed with no reference. Results of the fusion and distortion experiments indicate that the FFOCC is consistent with subjective evaluation. FFOCC significantly outperforms the other indices in evaluating fusion images that are produced by different fusion methods and that are distorted in spatial and spectral features by blurring, adding noise, and changing intensity. All the findings indicate that the proposed method is an objective and effective quality assessment for remote sensing image fusion.


Fig. 1 Original images and the results by different methods: (a) original PAN, (b) subset image of original PAN, (c) original MS, (d) subset image of MS, (e) result of BT, (f) subset image of BT,(g), result of IHS, (h) subset image of IHS, (i) result of PCA, (j) subset image of PCA, (k) result of WS, and (l) subset image of WS.







Fig. 2 Fusion experiments with more images: (a) SPOT PAN, (b) IKONOS PAN, (c) WorldView-2 PAN, (d) TM MS, (e) IKONOS MS, and (f) WorldView-2 MS.




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