Statistical power of intensity- and feature-based similarity measures for registration of multimodal remote sensing images

M. Uss; B. Vozel; V. Lukin; K. Chehdi

doi:10.1117/12.2240895

18 October 2016 Statistical power of intensity- and feature-based similarity measures for registration of multimodal remote sensing images

M. Uss, B. Vozel, V. Lukin, K. Chehdi

Author Affiliations +

Proceedings Volume 10004, Image and Signal Processing for Remote Sensing XXII; 1000403 (2016) https://doi.org/10.1117/12.2240895
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

This paper investigates performance characteristics of similarity measures (SM) used in image registration domain to discriminate between aligned and not-aligned reference and template image (RI and TI) fragments. The study emphasizes registration of multimodal remote sensing images including optical-to-radar, optical-to-DEM, and radar-to- DEM scenarios. We compare well-known area-based SMs such as Mutual Information, Normalized Correlation Coefficient, Phase Correlation, and feature-based SM using SIFT and SIFT-OCT descriptors. In addition, a new SM called logLR based on log-likelihood ratio test and parametric modeling of a pair of RI and TI fragments by the Fractional Brownian Motion model is proposed. While this new measure is restricted to linear intensity change between RI and TI (assumption somewhat restrictive for multimodal registration), it takes explicitly into account noise properties of RI and TI and multivariate mutual distribution of RI and TI pixels. Unlike other SMs, distribution of logLR measure for the null hypothesis does not depend on registration scenario or fragments size and follows closely chi-squared distribution according to Wilks’s theorem. We demonstrate that a SM utility for image registration purpose can be naturally represented in (True Positive Rate, Positive Likelihood Rate) coordinates. Experiments on real images show that overall the logLR SM outperforms the other SMs in terms of area under the ROC curve, denoted AUC. It also provides the highest Positive Likelihood Rate for True Positive Rate values below 0.4-0.6. But for certain registration problem types, logLR can be second or third best after MI or SIFT SMs.

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M. Uss, B. Vozel, V. Lukin, and K. Chehdi "Statistical power of intensity- and feature-based similarity measures for registration of multimodal remote sensing images", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000403 (18 October 2016); https://doi.org/10.1117/12.2240895

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