  Remote sensing is a higher technology to produce knowledge for data mining
applications. In principle hyperspectral images (HSIs) is a remote sensing tool
that provides precise classification of regions. The HSI contains more than a
hundred of images of the ground truth (GT) map. Some images are carrying
relevant information, but others describe redundant information, or they are
affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many
studies use mutual information (MI) or normalised forms of MI to select
appropriate bands. In this paper we design an algorithm based also on MI, and
we combine MI with steepest ascent algorithm, to improve a symmetric
uncertainty coefficient-based strategy to select relevant bands for
classification of HSI. This algorithm is a feature selection tool and a wrapper
strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial
intelligent system to control redundancy; we had to clear the difference of the
result's algorithm and the human decision, and this can be viewed as case study
which human decision is perhaps different to an intelligent algorithm. Index
Terms - Hyperspectral images, Classification, Fea-ture selection, Mutual
Information, Redundancy, Steepest Ascent. Artificial Intelligence
