Investigation of the impact of dimensionality reduction and feature selection on the classification of hyperspectral EnMAP data

In this paper, we address the classification of hyperspectral data which is comparable to the data acquired with the Environmental Mapping and Analysis Program (EnMAP)
mission, a hyperspectral satellite mission supposed to be
launched into space in the near future. While simulated
EnMAP data has already been released, only relatively few
studies have focused on investigating the performance of
approaches for classifying such EnMAP data. Hence, in a
recent paper, a contest for classifying EnMAP data has been
initiated to foster research about possible exploitation strategies.
Based on the dataset presented therein, we present a
framework involving techniques of dimensionality reduction,
feature selection and classification. We involve several classifiers
for pixelwise classification based on different learning
principles and investigate the impact of approaches for dimensionality
reduction and feature selection on the classification
results. The derived results clearly reveal the potential
of respective techniques and provide the basis for further
improvements in different research directions.