remote sensing, image registration, priori information, feature extraction
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, 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.
Tsinghua University Press
Xijia Liu, Xiaoming Tao, Ning Ge. Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction. Tsinghua Science and Technology 2016, 21(5): 552-560.