Combining Mutual Information and Scale Invariant Feature Transform for Fast and Robust Multisensor SAR Image Registration

Abstract

The Scale Invariant Feature Transform (SIFT) operator's success for computer vision applications makes it an attractive solution for the intricate feature based SAR image registration problem. For SAR images, SIFT feature matching results into lot of false alarms. To overcome the mentioned problem, we propose to use mutual information (MI) along with the SIFT operator for SAR image registration and matching applications. MI is an established multimodal registration similarity metric and has the capability to quickly estimate rough registration parameters from down-sampled images. The rough image registration parameters obtained using MI can be introduced for conjugate feature selection during the SIFT matching phase. Introduction of MI to the SIFT processing chain not only reduces the number of false alarms drastically but also helps to increase the number of matches as the operator detection and matching thresholds can be relaxed, relying on the available mutual information estimate. Further, the matching consistency of the SIFT matches especially for SAR images with various acquisition differences might not be up to the desired levels. To tackle the observed phenomenon, MI can further be utilized to refine the SIFT matches and to bring the matching consistency within desirable limits. We present our analysis based on multisensor, multitemporal and different view point SAR images acquired over plain and semi urban areas. The proposed registration methodology shows tremendous potential to become a fast and robust alternative for geometric SAR image registration as subpixel registration consistency has been achieved for diverse natured datasets.