ABSTRACT

Pansharpening is an important way of integrating spatial and spectral information in the field of remote sensing. This field uses the complementary and redundant information between multispectral (MS) images and panchromatic (PAN) images to obtain high spectral and high spatial resolution images. Various pansharpening methods have been introduced so far, each one attempting to provide a pansharpened image with the least distortion and maximum preservation of spectral and spatial information. Due to the importance of this issue, there should be methods and indices to evaluate the performance of different pansharpening algorithms and assess the quality of pansharpened images. In this paper, a segmentation-based method for assessing the quality of fused images is proposed. The advantage of this approach over pixel-based methods is that the pixel-based methods consider the fused images as a set of separate pixels while segmentation can take into account useful spatial information such as neighbourhoods, textures, etc. In the proposed method, by using k-means clustering algorithm, the reference and pansharpened images are segmented into areas with similar spectral and spatial features and the corresponding segments of the images are compared. This method is tested on three real data sets acquired by Pleiades, GeoEye-1, and QuickBird sensors. Experimental results demonstrate the effectiveness of the proposed method in evaluation of the quality of fused images.

摘要

Pansharpening是在遥感领域中整合空间和光谱信息的一种重要方式。该字段使用多光谱（MS）图像和全色（PAN）图像之间的互补和冗余信息来获得高光谱和高空间分辨率的图像。迄今为止，已经引入了各种全锐化方法，每种方法都试图提供具有最小失真并且最大程度地保留光谱和空间信息的全锐化图像。由于此问题的重要性，应该有一些方法和指标来评估不同的全锐化算法的性能并评估全锐化图像的质量。本文提出了一种基于分割的融合图像质量评估方法。与基于像素的方法相比，此方法的优势在于，基于像素的方法将融合图像视为一组单独的像素，而分段可以考虑有用的空间信息，例如邻居，纹理等。使用k均值聚类算法，将参考图像和锐化图像分割为具有相似光谱和空间特征的区域，并比较图像的相应片段。该方法在由Pleiades，GeoEye-1和QuickBird传感器获取的三个真实数据集上进行了测试。实验结果证明了该方法在融合图像质量评估中的有效性。在提出的方法中，通过使用k均值聚类算法，将参考图像和全清晰图像分割为具有相似光谱和空间特征的区域，并比较图像的相应片段。该方法在由Pleiades，GeoEye-1和QuickBird传感器获取的三个真实数据集上进行了测试。实验结果证明了该方法在融合图像质量评估中的有效性。在提出的方法中，通过使用k均值聚类算法，将参考图像和全清晰图像分割为具有相似光谱和空间特征的区域，并比较图像的相应片段。该方法在由Pleiades，GeoEye-1和QuickBird传感器获取的三个真实数据集上进行了测试。实验结果证明了该方法在融合图像质量评估中的有效性。