IEEE Geoscience and Remote Sensing Magazine - June 2021 - 3

FROM THE EDITOR
BY JAMES L. GARRISON
Introducing the June Issue
W
elcome to the June 2021 issue of IEEE Geoscience
and Remote Sensing Magazine! This is a special issue
on artificial intelligence (AI) innovation in geoscience
and remote sensing. As the volume of geoscience
data generated from remote sensing continues to grow
at an enormous rate, it becomes more and more important
to find better methods for extracting knowledge
from these data and using this knowledge to
make important decisions. The feature articles in this
special issue provide a snapshot of the current state of
the art in applying AI in the geoscience and remote
sensing fields.
To start off, I wish to thank the four guest editors,
who defined the scope of this issue, solicited white papers
and manuscripts, conducted the reviews, and selected
the best articles for inclusion. Dr. Sabah Mohammed
is a full professor in the Department of Computer
Science at Lakehead University (Ontario, Canada). Dr.
Tai-hoon Kim is a professor at Beijing Jiaotong University
(China) and a visiting scholar with the University
of Tasmania (Hobart, Australia). Dr. Pedram Ghamisi
is head of the Machine Learning Group at HelmholtzZentrum
Dresden-Rossendorf, Helmholtz
Institute
Freiberg for Resource Technology (Germany); cofounder
of VasoGnosis Inc. (San Jose, California, USA); and
a visiting professor with the Institute of Advanced Research
in Artificial Intelligence (Vienna, Austria). Dr.
Ruay-Shiung Chang is president of National Taipei
University of Business (Taipei City, Taiwan). Please see
their guest editorial on page 7 for a description of the
theme for this special issue and a summary of the selected
articles.
Three feature articles and one " Perspectives " column
form this special issue. The first feature is a comprehensive
review of AI in solving the phase unwrapping
Digital Object Identifier 10.1109/MGRS.2021.3078586
Date of current version: 17 June 2021
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
problem in interferometric synthetic aperture radar
(InSAR). The second feature addresses a different
aspect of SAR: applying deep learning methods to
image despeckling, including a review of the factors
that have limited the success of these approaches in
the past. Hyperspectral remote sensing is the subject
of the third feature, which addresses the use of
nonconvex modeling for such problems as image
restoration, dimensionality reduction, and spectral
unmixing. The " Perspectives " column rounds out
the special issue by presenting a position on future
research directions.
Additional content in this issue includes three
" Education " columns, two " Technical Committees "
columns, and one " Chapters " column. Our first " Education "
column describes the progress that has been
made in conducting remote sensing training in the
Spanish language. The NASA-funded Applied Remote
Sensing Training (ARSET) program has delivered live
and online educational programs in Spanish since
2009, reaching over 12,000 participants in 19 countries.
The column reviews ARSET's 10 best practices
for successful multilingual remote sensing training
and concludes with the results of a recent case study
in Bolivia, a nation where fewer than 5% of technical
experts are fluent in English. Continuing on the important
theme of expanding access to remote sensing
capabilities and knowledge, our second " Education "
column describes a successful program to develop a
low-cost camera lidar system through a collaboration
between Central Connecticut State University, the
National Oceanic and Atmospheric Administration,
and the University of the Bahamas. The authors describe
their model of sharing institutional expertise
and maintaining a single data analysis chain. Student
testimonials
support the value of this kind
of hands-on experience working with hardware and
learning proper research methodology to complement
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IEEE Geoscience and Remote Sensing Magazine - June 2021

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