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Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm
Hyperspectral images (HIS) classification is a high technical remote sensing
tool. The goal is to reproduce a thematic map that will be compared with a
reference ground truth map (GT), constructed by expecting the region. The HIS
contains more than a hundred bidirectional measures, called bands (or simply
images), of the same region. They are taken at juxtaposed frequencies.
Unfortunately, some bands contain redundant information, others are affected by
the noise, and the high dimensionality of features made the accuracy of
classification lower. The problematic is how to find the good bands to classify
the pixels of regions. Some methods use Mutual Information (MI) and threshold,
to select relevant bands, without treatment of redundancy. Others control and
eliminate redundancy by selecting the band top ranking the MI, and if its
neighbors have sensibly the same MI with the GT, they will be considered
redundant and so discarded. This is the most inconvenient of this method,
because this avoids the advantage of hyperspectral images: some precious
information can be discarded. In this paper we'll accept the useful redundancy.
A band contains useful redundancy if it contributes to produce an estimated
reference map that has higher MI with the GT.nTo control redundancy, we
introduce a complementary threshold added to last value of MI. This process is
a Filter strategy; it gets a better performance of classification accuracy and
not expensive, but less preferment than Wrapper strategy.Comment: 11 pages, 5 figures, journal pape
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