The image grand tour is a method for visualizing multispectral or multiple registered images. In many settings, several registered images of the same scene are collected. This most often happens when multispectral images are collected, but may happen in other settings as well. A multispectral image can be viewed as an image in which each pixel has a multivariate vector attached. The desired goal is to combine the multivariate vector into a single value which may be rendered in gray scale as an image. One way of exploring multivariate data has been by means of the grand tour. The grand tour in a conventional sense is a continuous space-filling path through the set of two-dimensional planes. Data is then projected into the two-planes. Traditionally the data analyst views the grand tour until an interesting configuration of the data is viewed. In our image grand tour, the grand tour is a continuous space filling path through the set of one-planes, i.e. lines. The idea of the image grand tour is then to project the vector attached to each pixel into the one-dimensional space and render each as a gray-scale value. Thus we obtain a continuously changing gray scale image of the multispectral scene. As with conventional data analysis, we watch the scene until an interesting configuration of the image is seen. In this talk we will discuss some of the theory associated with one-dimensional grand tours. We illustrate this talk with multispectral (six bands) images of minefield, and illustrate how the grand tour can create linear combinations of the multispectral images which specifically highlight mines in a minefield.
Edward J. Wegman,
Wendy L. Poston,
Jeffrey L. Solka,
"Image grand tour", Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323848; https://doi.org/10.1117/12.323848