Robust Multi-Sensor Image Alignment

This paper presents a method for alignment of images acquired by sensors of different modalities (e.g., EO and IR). The paper has two main contributions: (i) It identifies an appropriate image representation for multi-sensor alignment, i.e., a representation which emphasizes the common information between the two multi-sensor images, suppresses the non-common information, and is adequate for coarse-to-fine processing. (ii) It presents a new alignment technique, which applies global estimation to any choice of a local similarity measure. In particular, it is shown that when this registration technique is applied to the chosen image representation with a local-normalized-correlation similarity measure, it provides a new multi-sensor alignment algorithm which is robust to outliers, and applies to a wide variety of globally complex brightness transformations between the two images. Our proposed image representation does not rely on sparse image features (e.g., edge, contour, or point featu..