VOLUME 74, NUMBER 12
PHOTOGRAMMETRIC ENGINEERING & REMOTE
SENSING
JOURNAL OF THE AMERICAN SOCIETY FOR PHOTOGRAMMETRY
AND REMOTE SENSING
This month’s cover presents a complete 3D urban model of Toronto, Canada,
created with combined lidar datasets collected by three
of Optech Incorporated’s lidar mapping solutions:
ALTM Orion (an ultra-compact airborne mapping sensor),
LYNX Mobile MapperTM (a vehicle-mounted mobile
mapping sensor), and ILRIS 36D (a tripod-based long
range mapping sensor).
The background planimetric model represents data
collected by all three sensors, the airborne solution
being the dominant data source. Individual models at
opposite corners illustrate the level of detail and density
possible from inherently orthorectified and directly georeferenced
lidar data.
The upper right inset includes ILRIS 36D data of
Toronto’s CN Tower, requiring several vantage points
for total coverage that is not possible from the airborne
solution alone. The lower left inset is an example of the
street-level detail collected by the LYNX Mobile Mapper.
When lidar data from multiple platforms are wholly
combined, a true 3D model of the entire urban environment
is possible, including suspended power-lines,
complex structural detail, and relative surface responses
to the emitted wavelengths (i.e. intensity measures).
This trend towards combining multiple mapping platform types addresses the coverage limitations
of any single platform and responds to new market demands for ultra-dense, total-coverage lidar data.
For more information on this image collage, and the products used to generate them, contact
Michael Sitar, Product Manager, Optech Incorporated at michaels@optech.ca, or visit our website at
http://www.optech.ca.
Applying knowledge- and segment-based vertical stratification,
rule-based classification, and aggregation schemes with
knowledge-based correction to improve the classification accuracy
of urban features
Digital large-format photogrammetric sensors, the ADS40,
the DMC, and the UltraCamD are radiometrically calibrated
and characterized using a photogrammetric test field.
Using an ecological context to defi ne varying levels of landcover
class similarity, a decision framework guides map experts’ decisions and provides a more meaningful assessment
of map errors using fuzzy sets.
A theoretical investigation of the error budget of the iterative
algorithm in the mono-plotting process and estimates the algorithm-induced error in the ground coordinate output.
Validation of two theoretical models for estimating the reliability
of geometric accuracies measured as Root Mean Square Error
over corrected single images from QuickBird and Ikonos imagery.
Non-parametric Commitment and Typicality measures for the
fuzzy ARTMAP computational neural network to handle spatial
uncertainty in remotely sensed imagery classification.