ORBPC featurebased image registration In remote sensing, intensity variation and rotational distortion are unavoidable issues for multimodal image registration. To address the intensity variation issue, RadiationInvariant Feature Transform (RIFT) featurebased image registration method was developed. The RIFT produced good results for horizontal and vertical transformation in multimodal images. However, the extracted features in RIFT produce some feature matching errors due to the rotational distortion and hence reduces the registration accuracy. Oriented FAST and Rotated BRIEF (ORB) can produce better rotation invariant features since it was basically designed to deal with the mentioned issue. However, it is not suitable for solving the intensity variation problem. Therefore, in this research work, we integrated the Phase Congruence (PC) to ORB as PC is robust to illumination and intensity variations while the ORB is robust to rotational distortion. Hence the combination of both methods has been utilized to overcome all the mentioned issues simultaneously in order to enhance the accuracy of the image registration. Experiments were conducted in order to validate the superiority of the proposed method over recently introduced RIFT method in terms of accuracy.

Crossspectral registration of natural images with SIPCFE Image registration is a viable task in the field of computer vision with many applications. When images are captured under different spectrum conditions, a challenge is imposed on the task of registration. Researchers carefully handcraft a local module insensitive to illumination changes across crossspectral image pairs to tackle this challenge. We, in this paper, develop an optimized featurebased approach Single Instance Phase Congruency Feature Extractor (SIPCFE) to tackle the problem of natural crossspectral image registration. SIPCFE uses the phase information of an image pair to quickly identify and describe reliable keypoints that are insensitive to illumination. It then employs a sequence of outlier removal processes to find the matching feature points accurately and the Direct Linear Transformation to estimate the geometric transformation to align the image pair. We extensively study the proposed approach for every module in the system to give more insights into the challenges. We benchmark our proposed method and other stateoftheart featurebased methods developed for crossspectral imagery on three datasets with various settings and image contents. The comprehensive analysis of crossspectral registration results of natural images demonstrates that SIPCFE achieves up to 47.24%, 14.29%, and 12.45% accuracy improvement on the first, second, and third dataset, respectively, over the second best registration method in the benchmark.