PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
June 2014
479
PHOTOGRAMME TR I C ENG I NE ER I NG & REMOT E SENS I NG
The official journal for imaging and geospatial information science and technology
June 2014 Volume 80 Number 6
H I GHL I GHT ART I C L E
Roger M. Hoffer
I NT ERV I EW
PE ER - REV I EWED ART I C L ES
Pengjie Tao, Luping Lu, Yong Zhang, Biao Xu, and Songbai Zou
The use of public geographic data corrects the significant lens distortion and improves
the accuracy of direct georeferencing up to 115 m.
Iman Khosravi, Mehdi Momeni, and Maryam Rahnemoonfar
Comparing two pixel-based and two object-based building detection algorithms using a
diverse set of very high spatial resolution imagery.
Bo Yu, Li Wang, Zheng Niu, and Muhammad Shakir
A newly proposed method is not only effective in detecting built-up areas from images
with 10 m spatial resolution, but also performs well with 93.55 percent overall accuracy
utmost in our experiment.
Daniel M. Howard and Bruce K. Wylie
This study is an application of classification tree modeling using remote sensing and
ancillary environmental data to classify croplands of the U.S. Great Plains.
Dora Roque, Nuno Afonso, Ana M. Fonseca, and Sandra Heleno
Automatic delimitation of flooded areas in River Tagus, Portugal by applying Object-
based Image Analysis to SAR images and to a DTM.
Taoyang Wang, Guo Zhang, Deren Li, Xinming Tang, Yonghua Jiang, Hongbo Pan, and
Xiaoyong Zhu
A planar block adjustment and orthorectification of ZY-3 and evaluation of its geometric
accuracy.
COLUMNS
ANNOUNCEMENTS
Call for Papers
518
DEPARTMENTS
Calendar
550
Forthcoming Articles
558
Fifty years ago, in 1964,
for the first time ever, a
multispectral scanner
was flown over a
non-military target. The
analysis of this data and
subsequent research
at Purdue University to
develop computer-aided
analysis techniques
is described in the
Highlight Article this
month. The cover of this
month’s PE&RS shows a portion of a color photo that
was obtained simultaneously with data from a newly
developed 12-band multispectral scanner over a 70-
mile long, one-mile wide flight line in central Indiana
in 1967. This multispectral scanner data and the
associated photographs were obtained from a DC-3
operated by the Institute of Science and Technology,
University of Michigan (
Purdue
University scientists and engineers classified the
multispectral scanner data into spectrally distinct
cover types (i.e., green vegetation, bare soil and
water) using limited training data. The results
indicated that such computer-aided classifications,
using pattern recognition techniques, could be
achieved with reasonable accuracy. The red arrows
on this cover photo indicate locations that correspond
to the same location in the classified multispectral
scanner data
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