Automated TM/ETM+ image co-registration using pre-qualified area matching and studentized outlier detection

Abstract

Automated image co-registration is a process of matching an image to a reference image by deriving transformation parameters to shift co-incident points in the real world to match in the image space. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with Canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used to iteratively remove outliers at the level of three standard deviations. Through the pre-qualification and refining processes, the computation time was significantly improved and the registration accuracy enhanced. A prototype of the proposed algorithm was implemented and the performance test of 14 TM/ETM+ images in the USA showed: (1) average RMSE of the approach was 0.42 dependent upon terrain and features; (2) the average number of matching points was over 36 000; (3) the average processing time was less than 9.2 min per image using a workstation equipped with a 3.2 GHz Intel Pentium 4 CPU and 1 Gb Ram. The proposed approach achieved robustness, complete automation and reduced computation time.

Keywords:

• CO-REGISTRATION
• CANNY EDGE DETECTION
• AREA-BASED MATCHING
• STUDENTIZED RESIDUAL