Current PE&RS Cover
Photogrammetric Engineering & Remote Sensing In-Press Articles (Members Only)
NEW! Oct 2008 In Press Articles
As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS have been made available for members of the society.
Line Feature Correspondence between
Object Space and Image Space
Jen-Jer Jaw and Nei-Haur Perng
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Full Article (members only)
In recent years, linear features have been gradually integrated
into photogrammetry-related applications to estimate orientation
parameters and facilitate object reconstruction, to name
just a few. Yet, the correspondence of the conjugate linear
features between different spaces or coordinate systems
remains crucial to the goal of photogrammetric automation.
When establishing exterior orientation of images on the line
feature basis, the identifications and measurements of control
line features must be involved. In this study, the authors
develop an approach for effectively matching line features
between object space and image space. The proposed algorithms
start from projecting 3D line features into 2D image
space employing collinearity equations with approximate
orientation parameters. Then, the candidate lines detected
from the images are chosen and matched with the 3D line
features by imposing, in a sequential manner, geometric
constraints that include angle, distance, reference point, and
imaging geometry checks. Furthermore, a two-stage matching
strategy, where the outcome of the first matching stage by
partial 3D line features provides confined matching candidates
for the second matching stage, is found effective in
lowering the computational load, especially when faced
with a great amount of 3D line features. Preliminary tests
demonstrate successful, as well as satisfactory, 3D to 2D line
feature correspondences using the proposed approach and
strategy.
An Ecological Framework for Evaluating Map
Errors Using Fuzzy Sets
John H. Lowry, Jr., R. Douglas Ramsey, Lisa Langs Stoner, Jessica Kirby, and Keith Schulz
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Full Article (members only)
The use of fuzzy sets to assess uncertainty in land-use/cover
maps provides a robust conceptual framework for examining
unique characteristics of map error. By recognizing the
possibility of gradations of error, fuzzy sets can be used to
assess errors due to class similarity, or the sensitivity of the
map legend to class boundaries. Building on the theoretical
work of Gopal and Woodcock (1994), we present a practical
methodology for assessing map errors using fuzzy sets. A key
component of our methodology focuses on improving the
decision-making process map experts assume when conducting
a fuzzy set assessment of map errors. Using an ecological
context to define varying levels of land-cover class similarity,
we demonstrate how a decision framework guides the map
experts’ decisions and provides a more meaningful assessment
of map errors. Our methodology differs from traditional fuzzy
set error assessment methods in that the map expert evaluates
misclassifications within the error matrix (off-diagonal cells)
rather than individual reference sites. Advantages to a matrixbased
approach include a reduction in the time required by
map experts to evaluate map errors, and a relatively simple
means of conveying map error information to the map user.
We conclude that establishing criteria for determining multiple
set memberships in a fuzzy set error assessment is an important
methodological procedure that is commonly overlooked.
Our methodology, designed to explicitly identify land-cover
class similarities based on ecological criteria, serves as a
practical
example of how to address this issue.
A Knowledge-based Approach to Urban
Feature Classification Using Aerial Imagery
with Lidar Data
Ming-Jer Huang, Shiahn-Wern Shyue, Liang-Hwei Lee, and Chih-Chung Kao
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Full Article (members only)
While the spatial resolution of remotely sensed data has
improved, multispectral imagery is still not sufficient for
urban classification. Problems include the difficulty in
discriminating between trees and grass, the misclassification
of buildings due to diverse roof compositions and shadow
effects, and the misclassification of cars on roads. Recently,
lidar (light detection and ranging) data have been integrated
with remotely sensed data to obtain better classification
results. In this study, we first conducted maximum likelihood
classification (MLC) experiments, a traditional pixelbased
classification method, to identify features suitable for
urban classification using lidar data and aerial imagery. The
addition of lidar height data improved the overall accuracy
by up to 28 and 18 percent, respectively, compared to cases
with only red–green–blue (RGB) and multispectral imagery.
To further improve classification, we propose a knowledgebased
classification system (KBCS) that includes a three-level
height, “asphalt road, vegetation, and non-vegetation” (A–V–N) classification rule-based scheme and knowledgebased
correction (KBC). The proposed KBCS improved overall
accuracy by 12 and 7 percent compared to maximum
likelihood and object-based classification, respectively.
Incorporation of Flow Stripes as Constraints
for Calibrating Ice Surface Velocity
Measurements from Interferometric SAR Data
Hongxing Liu, Jaehyung Yu, Zhiyuan Zhao, and Kenneth C. Jezek
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Full Article (members only)
Derivation of absolute surface velocities from interferometric
SAR data requires velocity control points, and rock
outcrops or in situ GPS measurements are commonly
utilized as velocity control points in the calibration of
model parameters. However, it is often difficult or costly to
acquire sufficient number of such velocity control points
for the model calibration. This paper introduces ice flow
stripes as alternative control points for the calibration of
interferometric SAR data. Acquisition of this type of flow
direction control points is relatively easy and inexpensive.
We have derived the observation equations for the flow
direction control points and formulated the least squares
adjustment problem for calibrating the surface displace
measurements respectively derived from the conventional
interferometric method and speckle tracking method. Our
experiments with Radarsat interferometric SAR data in
Antarctica demonstrate that the exploitation of ice flow
stripes as control points are effective, practical, and
economical.
Classification of Very High Spatial Resolution
Imagery Based on the Fusion of Edge and
Multispectral Information
Xin Huang, Liangpei Zhang, and Pingxiang Li
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Full Article (members only)
A new algorithm based on the fusion of edge and multispectral
information is proposed for the pixel-wise classification
of very high-resolution (VHR) remotely sensed imagery. It
integrates the multispectral, spatial and structural information
existing in the image. The edge feature is first extracted
using an improved multispectral edge detection method,
which takes into account the original multispectral bands,
the linear NDVI, and the independent spectral components
extracted by independent component analysis (ICA).
Direction-lines are then defined using the edge and multispectral
information. Two effective spatial measures are
calculated based on the direction-lines in order to describe
the contextual information and strengthen the multispectral
feature space. Then, the support vector machine (SVM) is
employed to classify the hybrid structural-multispectral
feature set. In experiments, the proposed spatial measures
were compared with the pixel shape index (PSI) and the gray
level co-occurrence matrix (GLCM). The experimental results
show that the proposed algorithm performs well in terms of
classification accuracies and visual interpretation.
Designing a Multi-Objective, Multi-Support
Accuracy Assessment of the 2001 National
Land Cover Data (NLCD 2001) of the
Conterminous United States
Stephen V. Stehman, James D. Wickham, Timothy G. Wade, and Jonathan H. Smith
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Full Article (members only)
The database design and diverse application of NLCD 2001
pose significant challenges for accuracy assessment because
numerous objectives are of interest, including accuracy of
land-cover, percent urban imperviousness, percent tree
canopy, land-cover composition, and net change. A multisupport
approach is needed because these objectives require
spatial units of different sizes for reference data collection
and analysis. Determining a sampling design that meets the
full suite of desirable objectives for the NLCD 2001 accuracy
assessment requires reconciling potentially conflicting design
features that arise from targeting the different objectives.
Multi-stage cluster sampling provides the general structure to
achieve a multi-support assessment, and the flexibility to
target different objectives at different stages of the design. We
describe the implementation of two-stage cluster sampling for
the initial phase of the NLCD 2001 assessment, and identify
gaps in existing knowledge where research is needed to allow
full implementation of a multi-objective, multi-support
assessment.
Radiometric Calibration and Characterization
of Large-format Digital Photogrammetric
Sensors in a Test Field
Lauri Markelin, Eija Honkavaara, Jouni Peltoniemi, Eero Ahokas, Risto Kuittinen,
Juha Hyyppä, Juha Suomalainen, and Antero Kukko
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Full Article (members only)
Test field calibration is an attractive approach to calibrating
and characterizing the radiometry of airborne imaging instruments.
In this study, a method for radiometric test field
calibration for digital photogrammetric instruments is developed,
and it is used to evaluate the radiometric performance of
large-format photogrammetric sensors the ADS40, the DMC, and
the UltraCamD. In the study, linearity, dynamic range, sensitivity,
and absOlute calibration were evaluated. The results
demonstrated the high radiometric quality of the sensors
tested. All the sensors were linear in response. The DMC used
the 12-bit dynamic range entirely, while the ADS40 and the
UltraCamD indicated close to the 13-bit dynamic range. The
sensors performed quite differently with respect to sensitivity.
With the DMC and the UltraCamD a risk of overexposure
appeared, while the color channels of the ADS40 showed low
sensitivity. Because the sensors were linear in response, they
could be absolutely calibrated using linear models.
Calibration and Assessment of Multitemporal
Image-based Cellular Automata for Urban
Growth Modeling
Sharaf Alkheder and Jie Shan
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Full Article (members only)
This paper discusses the calibration and assessment of
a cellular automata model for urban growth modeling.
A number of transition rules are introduced in the cellular
automata model to consider the most influential urbanization
factors, such as land-cover maps obtained from satellite
images and population density from the census. The transition
rules are calibrated both spatially and temporally to
ensure the modeling accuracy. Spatially, each township
(about 6 miles 3 6 miles) in the study area is used as a
calibration unit such that the spatial variability of the urban
growth process can be taken into account. The temporal
calibration is performed by using a sequence of remote
sensing images from which the land-cover information at
different years is extracted. As for the assessment, fitness
(for urban level match) and two types of modeling errors
(for urban pattern match) are introduced as the evaluation
criteria. The study shows that the use of images reduces the
need for a large number of input data. Evaluation on the
rule variogram reveals that the transition rule values are
correlated spatially and vary with the urbanization level.
The paper reports the study outcome over the city of Indianapolis,
Indiana for the past three decades using Landsat
images and the population data.
Assessing Geometric Reliability of Corrected
Images from Very High Resolution Satellites
Manuel A. Aguilar, Fernando J. Aguilar and Francisco Agüera
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Full Article (members only)
Since the launch of Ikonos by Space Imaging, LLC on
24 September 1999, the very high resolution (VHR) satellite
imagery has been applied to diverse fields. Every application
needs a certain geometric accuracy in the corrected image;
therefore, the planimetric accuracy control of VHR satellite
imagery proves to be fundamental. As a rule of thumb, the
Root Mean Square error (RMS) computed at independent
check points (ICPs) is the global measure most widely used
for accuracy assessment in VHR imagery. This paper presents
an assessment, focused on two QuickBird and Ikonos
panchromatic single images, of the number of ICPs required
to obtain an estimation of one-dimensional accuracy (RMS1d)
with a certain confidence level or reliability. Thus, two
theoretical approaches have been tested to estimate reliability
depending on the number of ICPs, and they have been
experimentally validated using the Monte Carlo simulation
method. The residual’s samples were generated for both
satellite images in the best possible operational conditions:
(a) using optimal sensor models, (b) with high accuracy
ground points measured by Differential Global Positioning
System, (c) with an adequate number of well distributed
ground control points (GCPs), and (d) using GCPs and ICPs
well-defined on the raw images, i.e., with a reasonably low
pointing error. Under these conditions, the two theoretical
models tested provided a good fit (r2 > 97 percent) for the
simulated data offered by Monte Carlo when outliers were
withdrawn. There were no notable differences between the
results obtained from the Ikonos and Quickbird scenes.
Modeling Algorithm-induced Errors in
Iterative Mono-plotting Process
Yongwei Sheng
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Full Article (members only)
Mono-plotting from a single photo is an efficient method
to 3D spatial information collection. The core of the monoplotting
process is an algorithm to determine the ground
coordinates of pixel points in a single image by intersecting
the view ray with the DEM-defined surface. A traditional
algorithm used in photogrammetry is to iteratively calculate
the coordinates based on the inverse collinearity equations.
Being an iterative method, this algorithm may induce errors
to the derived coordinates. When the iteration precision (the
distance between the last two iterated points) becomes less
than a predefined precision threshold, the iteration is considered
convergent and the ground coordinates are produced.
However, the true error of the output coordinates may be still
worse than the threshold. This paper theoretically investigates
the error budget of the iterative algorithm, models the true
error from the iteration precision, and estimates the algorithm-induced error in the ground coordinate output.
Assessing Spatial Uncertainty of Lidar-derived
Building Model: A Case Study in Downtown
Oklahoma City
Mang Lung Cheuk and May Yuan
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Full Article (members only)
Light Detection and Ranging (lidar) technology enables costeffective
rapid production of digital models that capture
topography and vertical structures of surface features at a
fine spatial resolution. The capability has promoted lidar
applications for mapping terrain, buildings, forest stands, and
coastal features that cannot be adequately captured by other
remote sensing means over a large area. However, in complex
terrain, lidar data and lidar-derived products may contain
significant uncertainty. This research provides a simple
method to assess the spatial uncertainty of lidar-derived
building model, using downtown Oklahoma City, Oklahoma
as an example. Results indicate that significant uncertainty
could be found in urban environment where: (a) building
structures are complex, (b) buildings are constructed with
reflective materials, and (c) vegetation grows near-by. In
addition, cities under rapid development also challenge the
accuracy assessment of 3D building models. To conclude, we
suggest: (a) careful pre-flight planning before data collection,
(b) improve the feature extraction algorithm if possible,
(c) use of other remote sensing data, and (d) accuracy
assessment on suggested urban environments to reduce the
spatial uncertainty of lidar data and lidar-derived products.
Fuzzy ARTMAP Based Neurocomputational
Spatial Uncertainty Measures
Zhe Li
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Full Article (members only)
This paper proposes non-parametric measures for the fuzzy
ARTMAP computational neural network to handle spatial
uncertainty in remotely sensed imagery classification, i.e.,
ART Commitment (ART-C) and ART Typicality (ART-T), expressing
in the first case the degree of commitment a classifier has
for each class for a specific pixel, and in the second case,
how typical that pixel’s reflectances are of the ones upon
which the classifier was trained for each class. Results from
case studies were compared against the previously developed
SOM Commitment (SOM-C) and SOM Typicality (SOM-T) classifiers
as well as conventional Bayesian posterior probability and
Mahalanobis typicality soft classifiers. Principal Components
Analysis (PCA) was used to explore the relationship between
these different measures. Results indicate that ART-C and
SOM-C measures express values similar to Bayesian posterior
probabilies, and ART-T and SOM-T are closely related to
Mahalanobis typicalities. However, the proposed neural
approaches outperform the traditional methods due to their
non-parametric advantage.
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