1 research outputs found
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier
Over the past decades, the hyperspectral remote sensing technology
development has attracted growing interest among scientists in various domains.
The rich and detailed spectral information provided by the hyperspectral
sensors has improved the monitoring and detection capabilities of the earth
surface substances. However, the high dimensionality of the hyperspectral
images (HSI) is one of the main challenges for the analysis of the collected
data. The existence of noisy, redundant and irrelevant bands increases the
computational complexity, induce the Hughes phenomenon and decrease the
target's classification accuracy. Hence, the dimensionality reduction is an
essential step to face the dimensionality challenges. In this paper, we propose
a novel filter approach based on the maximization of the spectral interaction
measure and the support vector machines for dimensionality reduction and
classification of the HSI. The proposed Max Relevance Max Synergy (MRMS)
algorithm evaluates the relevance of every band through the combination of
spectral synergy, redundancy and relevance measures. Our objective is to select
the optimal subset of synergistic bands providing accurate classification of
the supervised scene materials. Experimental results have been performed using
three different hyperspectral datasets: "Indiana Pine", "Pavia University" and
"Salinas" provided by the "NASA-AVIRIS" and the "ROSIS" spectrometers.
Furthermore, a comparison with the state of the art band selection methods has
been carried out in order to demonstrate the robustness and efficiency of the
proposed approach.
Keywords: Hyperspectral images, remote sensing, dimensionality reduction,
classification, synergic, correlation, spectral interaction information, mutual
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