Hyperspectral Band Selection Using Statistical Models

Hyperspectral sensors are delivering a data cube consisting of hundreds of images gathered in adjacent frequency bands. Processing such data requires solutions to handle the computational complexity and the information redundancy. In principle, there are two different approaches deployable. Data compression merges this imagery to some few images. Hereby only the essential information is preserved. Small variations are treated as disturbances and hence removed. Band selection eliminates superfluous bands, leaving the others unmodified. Thus even minor deviations are preserved. In our paper, we present a novel band selection method especially developed for surveillance purposes. Hereby, the capability to detect even small variations poses an essential requirement, only fulfilled by the second approach. The computational complexity and the performance of such an algorithm depend on the available information. If complete knowledge about the targets and the background is available, contrast maximization establishes a perfect band selection. Without any knowledge the selection has to be performed by exploiting the band attributes often resulting in a poor choice. In order to avoid this, the developed algorithm incorporates the accessible information from the monitoring scene. In particular, features (e. g. anomalies) based on proximity relations are extracted in each band. Subsequently, an assessment of their suitability is accomplished by means of the value margins and the associated distributions. The final selection is then based on the inspection of the variations caused by the illumination and other external effects. We demonstrate and evaluate the appropriateness of this new method with a practical example.