PE&RS July 2017 Public - page 500

500 July 2017 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Photogrammetric Engineering & Remote Sensing (PE&RS)
Special Issue Call for Papers
“Computer Vision and Big Data Analytics for Remote Sensing”
Rapid advances in sensor technology, field robotics, unmanned aerial systems
(UAS), and computing power have facilitated exponential growth in remote
sensing applications. Meanwhile, processing complex, multiscale, and multidi-
mensional data from UAS, environmental sensors and climate model simula-
tions has become increasingly difficult for both scientists and public to summa-
rize and visualize the large amount of data for agricultural and environmental
assessments with direct applications to education, training and decision-mak-
ing. Data generated by thousands of images collected in a single UAS flight is
almost impossible to manually analyze. There exist both numerous opportu-
nities and challenges with broader usage of multi-sensor remote sensing data,
mostly attributed to processing algorithms of ultra-high resolution imagery,
translating the abundant spectral and spatial data to information useful for
decision-making. Computer vision, as a multidisciplinary field, seeks to mimic
human visual system for automatic extraction, analysis, and interpreting
useful information from images or a sequence of video or image frames. And
it offers promising analytical solutions for diverse fields of studies concerned
with Big Data. This Special Issue focuses on advancing research in computer
vision and big data analytics for remote sensing applications to address the
state-ofthe-art technical and application challenges.
Topics of Interest
The potential topics includes, but not limited to:
 Automatic crop monitoring using unmanned aerial systems and field
robots
 Precision Agriculture
 Aerial phenotyping
 Vegetation stress detection
 Computer vision based data fusion for remote sensing
 Object detection and classification using remotely sensed data
 Change detection using multi-temporal data
 Super-resolution image reconstruction
 Image contrast enhancement
 Image registration
 3D surface modeling and reconstruction
 High-performance computing paradigm for remote sensing applications
 Dimensionality reduction for remote sensing data
 Spatial and spectral feature extraction methods
 Noise modelling and estimation
 Big Data analytics
 Data visualization
 Augmented and Virtual Reality with remote sensing data
Submission Guideline
Innovative research, application, and comprehensive reviews are welcome to
present in this issue, however, the submitted manuscripts should not have
been published, nor be under consideration for publication elsewhere. All man-
uscripts will be peer-reviewed following the PE&RS reviewing procedures.
Submit your paper athttp://www.editorialmanager.com/asprs-pers/default.aspx.
Important Dates
Initial Submission
August 31, 2017
Review and Revision
October 31, 2017
Notification of Acceptance
November 15, 2017
Publication
December 31, 2017
Guest Editors
Dr. Wasit Wulamu
Associate Editor, PE&RS
Center for Sustainability
Saint Louis University,
Saint Louis, MO, USA
awulamu@slu.edu
www.remotesensinglab.org
Dr. Sidike Paheding
Center for Sustainability
Saint Louis University
Saint Louis, MO, USA
pahedings@slu.edu
www.remotesensinglab.org/paheding.html