基于异常区域感知的多时相高分辨率遥感图像配准

Registration for multi-temporal high resolution remote sensing images based on abnormal region sensing

在对多时相高分辨遥感图像进行配准时,由于成像条件差异,图像间存在的地物变化与相对视差偏移两类典型异常区域会影响配准精度。针对上述配准中存在的问题,提出一种基于异常区域感知的多时相高分辨率遥感图像配准方法,包括粗匹配和精配准两个阶段。尺度不变特征变换(SIFT)算法考虑到尺度空间属性,不同尺度空间提取的特征点在图像中对应不同大小的斑块,高尺度空间提取的特征点对应图像中的大斑点,其对应地物相对稳定、不易发生变化。首先,利用SIFT算法提取高尺度空间特征点完成图像快速粗匹配;其次,利用灰度相关性度量对图像块进行相对偏移量统计分类以感知视差偏移区域,同时结合空间约束条件,确定低尺度空间特征点的有效提取区域以及匹配点搜索范围,完成图像精配准。实验结果表明,将该方法用于多时相高分辨遥感图像配准,可有效抑制异常区域对特征点提取的影响进而提高配准精度。

In the processing of registration for multi-temporal high resolution remote sensing images, the phenomena of surface features change and relative parallax displacement caused by differences in acquisition conditions degrades the accuracy of registration. To resolve the aforementioned issue, a registration algorithm for multi-temporal high resolution remote sensing images based on abnormal region sensing was proposed, which consists of coarse and fine registration. The algorithm of Scale-Invariant Feature Transform （SIFT） has a better performance on scale space, the feature points from different scale space indicates the various size of spot. The high scale space points represent the objects which have a stable condition, the coarse registration can be executed depending on those points. For the fine registration, intensity correlation measurement and spatial constraint were used to decide the regions which were used to extract the efficacious points from low scale space, the areas for searching matching points were limited as well. Finally, the accuracy of the proposed method was evaluated from subjective and objective aspects. Experimental results demonstrate that the proposed method can effectively restrain the influence of abnormal region and improve registration accuracy.