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PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
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-
known surface characteristics is expressed by a weighted lin-
ear combination of elementary spectra with known land cover
class. They then determine the elementary spectra from image
reference data using archetypal analysis combined with a Re-
versible Jump Markov Chain Monte Carlo method.
The next group of two papers on 3D point classification starts
with Classification of aerial photogrammetric 3D point clouds
by Becker et al. The authors present a new method to classify
3D point clouds derived from aerial imagery which exploits
both geometric and colour information. They show that incor-
porating colour yields a significant increase in accuracy; the
approach can also be used to derive high accuracy digital ter-
rain models from digital surface models.
The contribution by Hackel et al. entitled Large-scale super-
vised learning for 3D point cloud labelling: SEMANTIC3D.NET
suggests a new benchmark data set for the classification of 3D
point clouds. Inspired by the recent success of deep learning
and Convolutional Neural Networks (CNNs), attributed to the
large number of employed training data, the authors hope to
boost 3D point classification in a similar way by providing a to-
tal of four billion manually labelled points for investigation by
the scientific community. They also describe some initial work
that under pins the expectations that CNNs might also lead to
very competitive results in this area.
The authors of the last paper of the first volume, Yang et al.,
work on the problem of building change detection. In 4D
Change detection based on persistent scatterer interferometry
- A case study of monitoring building changes they suggest to
track persistent scatters over time and to use them as indicators
for new and demolished buildings, respectively, in an automat-
ic statistics-based scheme. The new approach is successfully
evaluated based on simulations and on TerraSAR-X images.
The second volume stars with two papers on sensors. In the
first contribution, On a novel 360° panoramic stereo mobile
mapping system, Blaser et al. present a new mobile mapping
system equipped with different panoramic cameras which
achieves a full 360° multi-stereo coverage. The authors report
on system calibration and operational tests which yielded an
accuracy in the cm to dm range for both, relative and absolute
measurements.
The next paper, authored by Voges et al., deals with a particu-
larly important and often overlooked aspect for sensor system
calibration, namely time synchronisation. Using an example
from robotics the authors show how time offsets between dif-
ferent parts of the sensor system can be retrieved for SLAM
(simultaneous localisation and mapping) observations.
The next two papers are concerned with the non-semantic seg-
mentation of point clouds from different sources. The contri-
bution A voxel- and graph-based strategy for segmenting 3D
buildings scenes using perceptual grouping laws: comparison
and evaluation by Xu et al. presents two different segmentation
methods using voxel and supervoxel data structures, respec-
tively, by help of perceptual grouping. In experiments using
both laser scanning and photogrammetric point clouds the au-
thors could demonstrate high quality results also for complex
scenes and nonplanar object surfaces.
In their paper Range-image: Incorporating sensor topology for
lidar point clouds processing, Biasutti et al. take a different
view on LiDAR point cloud processing. Rather than working in
3D they project the 3D points into 2D space, arguing that in this
way the large amount of successful work on disocclusion from
images can be made use of. Based on these images a semi-au-
tomatic segmentation procedure based on depth histograms is
presented, and detected occluded areas are reconstructed using
a variational image inpainting technique.
The last two papers of this special issue tackle the problem of
object modelling. First, in Geometric reasoning with uncertain
polygonal faces, Meidow and Förstner discuss different strate-
gies which can help to strike a balance between too unspecif-
ic and too restrictive models. They then suggest to model and
to instantiate buildings as arbitrarily shaped polyhedra and to
recognize man-made structures in a subsequent stage by geo-
metric reasoning; examples are given to illustrate their method.
We hope that the reader will enjoy this variety to papers rang-
ing from sensor design to semantic image and point cloud pro-
cessing, and from novel scientific techniques to the investiga-
tion of data acquisition and processing systems. We would like
to sincerely thank everybody involved in the preparation of
this special issue. First of all and foremost, we are very grateful
to Alper Yilmaz, Editor-in-Chief of PE&RS, to have offered to
us the possibility to publish refined versions of the workshop
manuscripts in his journal, and for all the freedom we could
enjoy when preparing the special issue. We are very grateful to
the authors of this special issue for making available their ex-
cellent papers, and for keeping a tough timeline. We also wish
to wholeheartedly thank the reviewers of both, the workshop
and the journal papers, who have tremendously contributed to
improve the submitted manuscripts. We wish you, the read-
ers, an informative and enjoyable reading and hope that we
could reach the level of scientific excellence you expect from
this journal.
The Guest Editors: Christian Heipke, Karsten Jacobsen, and
Franz Rottensteiner (Hannover), Uwe Stilla (München), Mi-
chael Ying Yang (Enschede), Jan Skaloud (Lausanne), Ismael
Colomina (Casteldefels) and Michael Cramer (Stuttgart)
