The acquisition of large or high-resolution multispectral images may require that different parts of the scene be acquired separately and then be mosaicked to obtain the whole image. While the problem of stitching together parts of an image to form a consistent whole has been studied rather extensively for traditional images, in this case view angles are typically wide, geometrical distorsions are significant, and shots are often markedly misaligned with one another. On the other hand, many current applications for multispectral acquisition present a different scenario, as view angles are narrow, shots follow a much more precise alignment, and the quality of the resulting mosaic may be sensitive to even the tiniest registration errors, depending on the context. Moreover, given the nature of multispectral imaging, precision in color reproduction is usually much more important than it is when dealing with traditional images. All these issues raise the question of whether traditional strategies will work for typical multispectral images too.
In this paper, we report an experience in multispectral mosaicking. This experience was a first attempt at developing a mosaicking framework for multispectral images, including a suitable outline of the flow of operations as well as the selection of methods and procedures for geometrical registration and color synchronization. The scenario was that of an overhung camera acquiring images from above, with lighting provided by lamps used in professional photography; this can be seen as a typical studio setup, half-way between laboratory tests and 'field usage'. The performances of our framework in terms of quality of the resulting mosaics, resource requirements, and processing time, were evaluated through tests based on real acquisitions of different images.
One of the most important components in a multispectral acquisition system is the set of optical filters that allow acquisition in different bands of the visible light spectrum. Typically, either a rather large set of traditional optical filters or a tunable filter capable of many different configurations are employed. In both cases, minimising the actual number of filters used while keeping the error sufficiently low is important to reduce operational costs, acquisition
time, and data storage space. In this paper, we introduce the Filter Vectors Analysis Method for choosing an optimal subset of filters / filter configurations among those available for a specific multispectral acquisition system. This method is based on a statistical analysis of the data resulting from an acquisition of a representive target, and tries to identify those filters that yield the most information in the given environmental conditions. We have compared our method with a simple method (ESF, for 'evenly spaced filters') that chooses filters so that their transmittance peak
wavelengths are as evenly spaced as possible within the considered spectrum. The results of our experiments suggest that the Filter Vectors Analysis Method method can not bring substantial improvements over the ESF method, but also indicate that the ideas behind our method deserve further investigation.