Remote Sensing Image Registration With Adjustable Threshold and Variational Mixture Transformation

• Published in: IEEE geoscience and remote sensing letters Vol. 17; no. 5; pp. 765 - 769
• Main Authors: Ma, Xinke, Zhao, Wanjing, Hao, Xuying, Yang, Yang, Yang, Kun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adjustable threshold strategy (ATS); Algorithms; Estimation; Feature extraction; Gaussian mixture model; Genetic transformation; guided Gaussian mixture model (GGMM); Image registration; Image sensors; Probabilistic models; Registration; Remote sensing; remote sensing (RS) image registration; Sensors; Transformations; variational mixture transformation (VMT)
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2019.2936396

This letter proposes a remote sensing (RS) image registration method based on adjustable threshold and variational mixture transformation (VMT). The main contributions of our method are: 1) an adjustable threshold strategy proposed to guarantee sufficient inlier pairs for image spatial transformation and 2) a VMT, which achieves a coarse-to-fine process, consisting of three main steps as follows: a) a rigid transformation is employed to achieve approximate correspondence; b) a guided Gaussian mixture model is proposed to better distinguish outliers; and c) a nonrigid transformation is applied to achieve an accurate registration. We test the performance of our algorithm in feature matching and RS image registration and compare it with the six state-of-the-art methods. Our method shows the best performances in most scenarios.