Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction

• 作 者: Xijia Liu Xiaoming Tao Ning Ge
• 机 构: the Department of Electronic Engineering, Tsinghua University,
• 出 处: Tsinghua Science and Technology
• 发表日期: 2016年05期
• 关键词: registration matching priori hierarchical neighborhood apply captured maintainin

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform(SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7:47 while maintaining the accuracy of the image registration performance.

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images, and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art Scale Invariant Feature Transform (SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7:47 while maintaining the accuracy of the image registration performance.