FIRM: Framework for Image Registration Using Multistage Feature Detection and Mode-Guided Motion Smoothness Keypoint Optimization

Abstract

Remote-sensing image registration is a pivotal preprocessing step for earth observation data analytics. In this regard, georeferencing corrects the systematic geometric degradation in the image. However, it is difficult to achieve subpixel geometric accuracy across multitemporal scenes. This article focuses on hindmost part of geometric correction that uses reference layer and feature detection in hierarchical stages to improve the geometric fidelity of images at subpixel level. The methodology developed is based on patch affine-oriented fast and brief with mode-guided tiled scale invariant feature transform (MT-SIFT) techniques in a coordinate manner at a multistage processing architecture, which we refer to as FIRM. Motion smoothness constraint (MSC) keypoint correspondence optimization is used in FIRM to remove the outliers at gross stage and estimate segmented affine transformation parameters at finer stage. The automatic coregistration pipeline is evaluated in Indian Resourcesat multispectral camera images covering diverse landscapes. The capability of the designed framework is demonstrated to handle relatively large geometrical error. With more than a decade difference in acquisitions, multitemporal images are superimposed over each other and compared with state-of-the-art feature-based methods. The potential of the proposed approach FIRM is assessed on multisatellite imagery acquired from Resourcesat-2 and Landsat −8. It is observed that the root mean square error (RMSE) between coregistered images is 0.12 pixel at a spatial resolution of 5 m.