The proposed reduction approach is deterministic and iterative. It includes a connectivity criterion between bands which uses the Manhattan distance. This criterion allows the automatic partitioning of M spectral bands, leading to an identification of the most relevant spectral bands to keep in the further pixel classification process. Moreover, the use of this criterion avoids classes with only one band. The spectral band selected to represent a given class is the closest to all the other bands of this class, with respect to the used metric.
The spectral bands reduction developed has been evaluated and validated with our unsupervised descending hierarchical classification pixel method (UDHC), with the addition of a regularization step. A real hyperspectral image composed of 100 spectral bands has been used for the experimental study.