title: An Improved InSAR Image Co-Registration Method for Pairs with Relatively Big Distortions or Large Incoherent Areas.
abstract: Co-registration is one of the most important steps in interferometric synthetic aperture radar (InSAR) data processing. The standard offset-measurement method based on cross-correlating uniformly distributed patches takes no account of specific geometric transformation between images or characteristics of ground scatterers. Hence, it is inefficient and difficult to obtain satisfying co-registration results for image pairs with relatively big distortion or large incoherent areas. Given this, an improved co-registration strategy is proposed in this paper which takes both the geometric features and image content into consideration. Firstly, some geometric transformations including scale, flip, rotation, and shear between images were eliminated based on the geometrical information, and the initial co-registration polynomial was obtained. Then the registration points were automatically detected by integrating the signal-to-clutter-ratio (SCR) thresholds and the amplitude information, and a further co-registration process was performed to refine the polynomial. Several comparison experiments were carried out using 2 TerraSAR-X data from the Hong Kong airport and 21 PALSAR data from the Donghai Bridge. Experiment results demonstrate that the proposed method brings accuracy and efficiency improvements for co-registration and processing abilities in the cases of big distortion between images or large incoherent areas in the images. For most co-registrations, the proposed method can enhance the reliability and applicability of co-registration and thus promote the automation to a higher level.
authors.affiliation: School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China. guanyuechen@whu.edu.cn.
authors.forename: Zhenwei
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong. lslzhang@polyu.edu.hk.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: State Key Laboratory of Information Engineering in Survey, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China. guozhang@whu.edu.cn.
authors.forename: Guo
authors.initials: G
authors.lastname: Zhang
keywords: InSAR; co-registration; non-translation relation; registration point