基于HVS结构相似度的极化SAR图像质量评价方法

通过人工解译对极化SAR图像进行地物分类，可提高测绘产品的生产效率。极化SAR图像质量评价是一项重要工作，然而传统的评价方法在针对极化SAR图像时未能充分考虑极化SAR图像的伪彩色特征与边缘信息特征，也未能满足人眼视觉系统（HVS）的特殊需求。因此，提出了一种基于HVS结构相似度（SSIM）的极化SAR图像质量评价方法。该方法对传统算法进行了改进，综合考虑了HVS特性与极化SAR图像的特点。实验证明该方法符合HVS的特性，而且能够针对极化SAR图像的特点给出准确的评价结果。

Improved method of polarimetric SAR image quality assessment based on human visual system and structural similarity

To improve the efficiency of surveying and mapping production, manual interpretation of polarimetric synthetic aperture radar (SAR) images have been widely used in the classification of surface features. Polarimetric SAR image quality assessment has become a significant study field. However, ordinary image quality assessment methods failing to take full account of the characteristics of pseudo color and edge detection in polarimetric SAR images, neither takes account of the special needs of human visual system (HVS). Thus we advance a method of polarimetric SAR image quality assessment based on HVS and Structural Similarity (SSIM). It is improved by the ordinary image quality assessment and combines the feature of HVS and polarimetric SAR images. It is demonstrated by the experiments that this method could fit the features of HVS and provide an accurate image quality assessment focusing on the characteristics of polarimetric SAR images.