Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

The scale-invariant feature transform (SIFT) ability to automatic control points (CPs) extraction is very well known on remote sensing images, however, its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs, their matching has high false alarm. This paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference (SAD) in different manner for the new optical satellite generation called near-equatorial orbit satellite (NEqO) and multi-sensor images. The proposed method leads to improving CPs matching with a significantly higher rate of correct matches. The data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan area. The proposed method consists of three parts: (1) applying the SIFT to extract CPs automatically, (2) refining CPs matching by SAD algorithm with empirical threshold, and (3) evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed method. The result indicates an accurate and precise performance of the model, which showed the effectiveness and robustness of the proposed approach.