Spectral discrimination based on the optimal informative parts of the spectrum

Developments in sensor technology boost the information content of imagery collected by space-and airborne hyperspectral sensors. The sensors have narrow bands close to each other that may be highly correlated, which leads to data redundancy. This paper first shows a newly developed method to identify the most informative spectral regions of the spectrum with the minimum dependency with each other, and second evaluates the land cover class separability on the given scenes using the constructed spectral bands. The method selects the most informative spectral regions of the spectrum with defined accuracy. It is applied on hyperspectral images collected over three different types of land cover including vegetation, water and bare soil. The method gives different band combinations for each land cover showing the most informative spectral regions; then a discrimination analysis of the available classes in each scene is carried out. Different separability measures based on the distribution of the classes and scatter matrices were calculated. The results show that the produced bands are well-separated for the given classes.