Title:Robust registration for remote sensing images by combining and localizing feature- and area-based methods

Authors:Ruitao Feng, Qingyun Du, Xinghua Li, Huanfeng Shen

Source:ISPRS Journal of Photogrammetry and Remote Sensing Volume：151 Pages：15-26 Published：MAY 2019

Abstract:Highly accurate registration is one of the essential requirements for numerous applications of remote sensing images. Toward this end, we have developed a robust algorithm by combining and localizing feature- and areabased methods. A block-weighted projective (BWP) transformation model is first employed to map the local geometric relationship with weighted feature points in the feature-based stage, for which the weight is determined by an inverse distance weighted (IDW) function. Subsequently, the outlier-insensitive (OIS) model aims to further optimize the registration in the area-based stage. Considering the inevitable outliers (e.g., cloud, noise, land-cover change), OIS integrates Huber estimation with the structure tensor (ST), which is an approach that is robust to residual errors and outliers while preserving edges. Four pairs of remote sensing images with varied terrain features were tested in the experiments. Compared with the-state-of-art algorithms, the proposed algorithm is more effective, in terms of both visual quality and quantitative evaluation.

Keywords plus:Block-weighted model; Huber estimation; Outlier-insensitive model; Registration; Structure tensor; Remote sensing image

E-mail:lixinghua5540@whu.edu.cn (X. Li), shenhf@whu.edu.cn (H. Shen).

Addresses:

[Ruitao Feng, Qingyun Du, Huanfeng Shen]School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

[Xinghua Li]School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

[Qingyun Du, Huanfeng Shen]Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

[Qingyun Du, Huanfeng Shen]Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China

Impact factor：5.994