IEEE Geoscience and Remote Sensing Magazine - December 2020 - 60

Feature Extraction
for Hyperspectral
Imagery
The evolution from shallow to deep: Overview and toolbox

©SHUTTERSTOCK/MUNIMARA

BEHNOOD RASTI, DANFENG HONG, RENLONG HANG, PEDRAM GHAMISI,
XUDONG KANG, JOCELYN CHANUSSOT, AND JON ATLI BENEDIKTSSON

H

yperspectral images (HSIs) provide detailed spectral
information through hundreds of (narrow) spectral
channels (also known as dimensionality or bands), which can
be used to accurately classify diverse materials of interest.
The increased dimensionality of such data makes it possible
to significantly improve data information content but provides a challenge to conventional techniques (the so-called
curse of dimensionality) for accurate analysis of HSIs.
Feature extraction (FE), a vibrant field of research in
the hyperspectral community, evolved through decades of
research to address this issue and extract informative features suitable for data representation and classification. The
Digital Object Identifier 10.1109/MGRS.2020.2979764
Date of current version: 8 June 2020

60

0274-6638/20©2020IEEE

advances in FE were inspired by two fields of research-the
popularization of image and signal processing along with
machine (deep) learning-leading to two types of FE approaches: the shallow and deep techniques. This article outlines the advances in these approaches for HSI by providing
a technical overview of state-of-the-art techniques, offering
useful entry points for researchers at different levels (including students, researchers, and senior researchers) willing to
explore novel investigations on this challenging topic.
In more detail, this article provides a bird's eye view of
shallow [both supervised FE (SFE) and unsupervised FE
(UFE)] and deep FE approaches, with a specific focus on
hyperspectral FE and its application to HSI classification.
Additionally, this article compares 15 advanced techniques
with an emphasis on their methodological foundations and
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

DECEMBER 2020



IEEE Geoscience and Remote Sensing Magazine - December 2020

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2020

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