PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
June 2018
345
Editorial
Best of “ISPRS Hannover Workshop 2017”
Guest Editors Christian Heipke, Karsten Jacobsen, and Franz Rottensteiner, Uwe Stilla,
Michael Ying Yang, Jan Skaloud, Ismael Colomina, and Michael Cramer
Sensor calibration, image orientation, object extraction and
scene understanding from images and image sequences are im-
portant research topics in Photogrammetry, Remote Sensing,
Computer Vision and Geoinformation Science, the areas of
interest of the International Society for Photogrammetry and
Remote Sensing (ISPRS). Within these areas, both geometry
and semantics play an important role, and high quality results
require appropriate handling of all these aspects. While indi-
vidual algorithms differ according to the imaging geometry and
the employed sensors and platforms, all mentioned aspects
need to be integrated in a suitable workflow to solve most re-
al-world problems.
This observation led to the organization of a common event for
a number of well-established scientific meetings under the roof
of the ISPRS Hannover Workshop, held in Hannover, Germany
from June 6 – 9, 2017. These meetings were
• HRIGI - High-Resolution Earth Imaging for Geospatial
Information, which has been held in Hannover every two
years since the middle of the 1990’s,
• CMRT - City Models, Roads and Traffic, a workshop deal-
ing with automatic object extraction in urban environ-
ments with a first edition in 2005,
• ISA - Image Sequence Analysis, a relatively new work-
shop focussing on images sequences,
• EuroCOW - European Calibration and Orientation Work-
shop, looking specifically at sensors, calibration and ori-
entation, which had previously been held in Barcelona,
Spain for many years.
While HRIGI and EuroCOW are more on the geometric side,
CMRT and ISA have a legacy in automatic object reconstruc-
tion and trajectory computation. The aim of the common event
was to seek, exploit and deepen the synergies between geom-
etry, semantics and sensor modelling, and to give the different
scientific communities the possibility to discuss with, and to
learn from, each other. The joint event was supported by 12
working groups from four of the five ISPRS Technical Commis-
sions
1
and addressed experts from research, government, and
private industry. It consisted of high quality papers, and pro-
vided an international forum for discussion of leading research
and technological developments, as well as applications in the
field.
Following the workshop, authors whose contributions were
accepted after a full-paper double blind review were invited
to revise and extend their papers in the light of the discussions
at the workshop and to submit them to a special issue of Pho-
togrammetric Engineering & Remote Sensing. Sixteen papers
were submitted and after another round of scientific reviews,
eleven of them were finally accepted for publication in this
special issue. This large number constitutes a major success
and demonstrates both, the relevance of the addressed topics
and the high quality of the manuscripts; it has also led to the
fact that the special issue had to be distributed to two volumes.
The first volume contains papers related to the classification
of images (three papers) and point clouds (two papers) and to
change detection (one paper). The first two papers of the sec-
ond volume deal with sensor design and calibration, the fol-
lowing two with point cloud segmentation and the last two
with the modelling of specific topographic objects (buildings
in this case).
The first paper, authored by Vogt et al. and entitled Unsuper-
vised source selection for domain adaptation deals with trans-
fer learning, i.e. the question to which extent training data from
one geographic area or epoch (called source) can be employed
to classify data of another area or epoch (target) even if the fea-
tures in the target image follow a slightly different distribution.
More specifically, the best among many available sources for
a specific classification problem is determined based on simi-
larity measurements between the marginal distributions of the
features in the source and various target domains.
The second paper, Multitemporal classification under label
noise based on outdated maps by Maas et al. is devoted to the
problem arising from incorrect training data. While for map
updating abundant training data are available in the form of the
(outdated) map itself, some for the training data are incorrect
and result in wrong classification results. The authors develop
a new noise tolerant classification method that can also con-
sider the outdated map as prior information and show that it
helps to distinguish between real changes over time and false
detections caused by misclassification.
The paper by Drees and Roscher, Archetypal analysis for sparse
representation-based hyperspectral sub-pixel quantification,
suggests a new classification method for hyper-spectral image
data. Typically these data have a rather coarse geometrical res-
olution resulting in mixed pixels (pixels containing more than
one spectral class). The authors develop a new constrained
sparse representation of the data, where each pixel with un-
Photogrammetric Engineering & Remote Sensing
Vol. 84, No.6, June 2018, pp. 345–346.
0099-1112/18/345–346
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.6.345
