Data fusion of multi-source imagery based on linear features registration

Abstract

Image fusion is the process of combining images captured by different sensors under different conditions. These images usually have different geometric and radiometric properties. The enormous increase in the volume of remotely sensed data has created the need for robust data processing techniques that can fuse data observed by different acquisition systems. This need is motivated by the fact that collected data by these sensors are complementary in nature. Therefore, simultaneous utilization of the collected data would guarantee a full understanding of the object/phenomenon under consideration. In this regard, a data-fusion procedure can be defined as being concerned with the problem of how to combine data and/or information from multiple sensors in order to achieve improved accuracies and better inference about the environment than could be attained through the use of a single sensor. Fusion of multi-source imagery captured under different conditions is a challenging problem. The difficulty is attributed to the varying radiometric and geometric resolutions of the acquired imagery. Image registration is considered as one of the most critical requirement for accurate data fusion. The most appropriate primitives, transformation function and similarity measure have been incorporated in a matching strategy to solve the registration problem. Experimental results using real data proved the feasibility and the robustness of the suggested method.