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A MVG-based non-reference quality evaluation method for Pan/MS Fusion

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：a13726327238@163.com
  • Introduction：包科迪，研究方向为数字图像处理及图像质量评价。E-mail：a13726327238@163.com
BAO Kedi,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：mengxiangchao@nbu.edu.cn
  • Introduction：孟祥超，研究方向为多源遥感数据融合与应用。E-mail：mengxiangchao@nbu.edu.cn
MENG Xiangchao*,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

SHAO Feng,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

YE Mengmeng,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

JIN Kangjun,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

PENG Zhenyu

résumé

Pansharpening aims to sharpen a Low-spatial-Resolution (LR) multispectral (MS) image using a High-spatial-Resolution (HR) panchromatic (PAN) image to obtain a HR MS image. The pansharpened image with high spatial resolution and spectral fidelity had a wide application prospect. However, on the one hand, pansharpening methods usually produce different spatial or spectral distortions; on the other hand, the real HR MS reference image for the quality evaluation of the fused image cannot be obtained due to the limitation of satellite imaging systems. Therefore, the effective evaluation the quality of the fusion image without the real reference image is of great significance.In this paper, we propose a new non-reference quality evaluation method for pansharpened image on the basis of the Multivariate Gaussian Model (MVG). On the basis of a large-scale pansharpening database, this study constructed benchmark, test, and verification data sets, including various satellite sensors and thematic types. Then, a novel benchmark MVG evaluation model was constructed on the basis of the benchmark data set. In the proposed method, the images were first divided into sub-image blocks, and the spatial and spectral sensitive features of each sub-image block were first extracted. Then, many spatial and spectral characteristics were trained to establish the benchmark MVG evaluation model. In addition, in the benchmark MVG training, the sub-image with a high variance was selected for model training to enhance the robustness of the benchmark MVG model. Then, the testing MVG evaluation model for the fused image was established by comprehensively considering the spatial and spectral distortions. Finally, the relative distance between the benchmark and testing MVG models of the fused image was used to calculate the fused image quality.Experimental results show that the proposed method has better performance than the traditional non-reference quality evaluation methods in most satellite- and thematic-based data sets. The proposed method is based on Wald’s protocol and cannot be extended well to the full resolution evaluation of the fused image. Therefore, in future work, we will continue to study the full resolution evaluation method for pansharpening, which is more challenging and meaningful.

mots-clés

remote sensing;image fusion;non-reference quality evaluation;Multivariate Gaussian Model (MVG);panchromatic image;multispectral image

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