基于Sketch Token的异源雷达影像匹配适应性分析

将异源影像间的共性信息提取与描述作为研究重点，提出了一种基于Sketch Token中层特征的异源影像适配性评估方法.该算法利用监督学习策略获取异源雷达影像之间共性特征的先验知识，据此训练Sketch Token特征分类器，并将其作为影像间共性特征的描述子对基准图进行特征提取.利用提取结果的各项统计量训练支持向量机分类器，并以此评估异源影像对中基准图的适配性.利用星载SAR(synthetic aperture radar)影像作为基准图、机载实孔径雷达影像作为实时图进行适配性评估试验，试验结果表明了该算法的有效性.
Focusing on the extraction and description of common information between heterologous images, this paper proposed an algorithm for matching suitability analysis between heterologous images based on Sketch Token middle level features. Supervised learning has been used to acquire priori knowledge of homogeneous information between heterologous images, and utilize this knowledge to train the Sketch Token classifier, which is designed as a descriptor of homogeneous information to extract features on reference images. The statistical of extracted features is applied for training support vector machine classifier, which is used to analysis the matching suitability of reference images. The algorithm is validated by the satellite SAR(synthetic aperture radar) images which have been used as reference images and aerial RAR(real aperture radar) images which have been used as real images. The test result demonstrate the effectiveness of the algorithm