PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
February 2014
111
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
February 2014 Volume 80 Number 2
F EATURE ART I C L E
I N MEMOR I AM
PE ER - REV I EWED ART I C L ES
Yong Li, Bin Yong, Huayi Wu, Ru An, Hanwei Xu, Jia Xu, and Qisheng He
A novel algorithm that incorporates modified white top-hat transform and directional
edge constraints to simultaneously consider the size, height, and edge characteristics of
point clouds for airborne lidar data filtering.
Jacob L. Strunk, Hailemariam Temesgen, Hans-Erik Andersen, and Petteri Packalen
A study to develop and evaluate a strategy to integrate field plots with strips of lidar
and Landsat to support mapping and estimation of forest attributes.
Liang Cheng, Lihua Tong, Manchun Li, and Yongxue Liu
A method to extract parking lot structures from an aerial orthophoto with high
correctness, high completeness, and good geometric accuracy.
Fatemeh Tabib Mahmoudi, Farhad Samadzadegan, and Peter Reinartz
A multi-agent system for performing higher levels of modification on the results of object
based image analysis based on the capabilities of WorldView-2 satellite imagery.
Inseong Jeong and James Bethel
An automatic parameter selection procedure for satellite pushbroom sensor models is
developed and evaluated by comprehensive experimental results.
Aaron E. Maxwell, Timothy A. Warner, Michael P. Strager, and Mahesh Pal
When mapping land-cover within a mountaintop coal surface mine permit, the
incorporation of lidar-derived data increased classification accuracy in comparison to
using only the five image bands from RapidEye; support vector machines (SVM) generally
produced a more accurate classification than ensemble decision tree algorithms.
COLUMNS
ANNOUNCEMENTS
DEPARTMENTS
This image of
a ponderosa
pine tree from
the Colorado
Front Range was
collected using
a ground-based
lidar instrument.
The lidar point
cloud data were
then converted to
volumetric pixels,
or voxels, as
part of research
on new methods of 3-D visualization, modeling
and analyses of lidar data. The U.S. Geological
Survey (USGS) is collecting and using 3-D data
from lidar and other instruments mounted on many
different platforms, such as aircraft, tripods, cars
and all-terrain vehicles, and even from balloons and
UASs in their research and applications. Scientists
at USGS are using extremely high-resolution lidar
and other 3-D data collected from these various
platforms for a wide variety of studies, including
earthquake fault and landslide mapping, biomass
and habitat estimations, fire behavior modeling,
validation of airborne remote sensing data, and
coastal sea level rise and storm surge simulations.
For more information about the various ways
USGS is collecting and using lidar and other 3-D
data, contact Jason Stoker at
.
