A pixel‐based semi‐stochastic algorithm for the registration of geophysical images
Alexandra A. Karamitrou, Gregory N. Tsokas, Maria Petrou · Archaeological Prospection · 2017
Abstract The availability of overlapping geophysical data produced by different sensors provides complementary information about the investigation area. However, joint interpretation of these geophysical images is challenging. One common problem is the registration of the images that is necessary to compare features appearing in dissimilar datasets. Measurements in archeological geophysics are often performed by handheld devices therefore, the actual location of the measurement could be different from the planned one. These offsets are localized and essentially random. Consequently, it is impossible to correct them following usual deterministic approaches. This paper presents a novel registration method between geophysical images produced from different prospecting methods. We developed a semi‐stochastic, iterative registration algorithm that applies random local transformations in small randomly selected regions of the processed image. The algorithm uses the mutual information of the images as similarity measure due to its suitability in images of different modalities. We use a pair of images to train the algorithm and tune its parameters. Afterwards, we test the method with nine different pairs of geophysical images from various locations and characteristics. The results, in all cases, show a significant increase of the mutual information in comparison with the registration through geographical coordinates.