Peer-Reviewed Articles
361 A Comparison of Four Common Atmospheric
Correction Methods
Abdolrassoul S. Mahiny and Brian J. Turner
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Four atmospheric correction methods, two relative and two
absolute, were compared in this study. Two of the methods
(PIF and RCS) were relative approaches; COST is an absolute
image-based method and 6S, an absolute modeling method.
The methods were applied to the hazy bands 1 through 4
of a Landsat TM scene of the year 1997, which was being
used in a change detection project. The effects of corrections
were studied in woodland patches. Three criteria, namely
(a) image attributes; (b) image classification results, and
(c) landscape metrics, were used for comparing the performance of the correction methods. Average pixel values,
dynamic range, and coefficient of variation of bands constituted the first criterion, the area of detected vegetation
through image classification was the second criterion, and
patch and landscape measures of vegetation the third
criterion. Overall, the COST, RCS, and 6S methods performed
better than PIF and showed more stable results. The 6S
method produced some negative values in bands 2 through
4 due to the unavailability of some data needed in the
model. Having to use only a single set of image pixels
for normalization in the PIF method and the difficulty of
selecting such samples in the study area may be the reasons
for its poor performance.
369 A Rigorous Laboratory Calibration Method for Interior
Orientation of an Airborne Linear Push-Broom Camera
Tianen Chen, R yosuke Shibasaki, and Zongjian Lin
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The linear push-broom camera is one of the most important
imaging systems in modern photogrammetry and remote
sensing to collect high-resolution digital panchromatic and
multispectral images for comprehensive evaluation of
natural resources and environmental conditions. Most
commercial high-resolution satellites such as SPOT-5, Ikonos-2,
and QuickBird use similar sensors. Airborne sensors such
as the ADS40 and STARIMAGER® also use the push-broom
approach to collect multi-channel, seamless image strips for
linear objects such as roads, railways, rivers, seashores,
electric power lines, pipe lines, and high-rise building areas.
Other commercial hyperspectral sensors also use the push-broom approach to collect digital images for remote sensing
applications.
High performance linear cameras require high resolution geometric, spectral, and radiometric calibration. This topic has not been covered in the scientific literature. Although the self-calibration is successful to compute the calibration parameters of frame camera, it is denied in our tests since the linear camera’s calibrated parameters are dependent on the accuracy of on-board GPS/IMU position and attitude data. In this paper, a rigorous calibration method is presented. This approach has been successfully used in our developed airborne three-line scanner (TLS) imaging system STARIMAGER® in the last five years. The method can accurately compute TLS’s focal length, principle point location, lens distortion, CCD pixel size, CCD curve deformation, and convergence angles between each CCD line sensor on the focal plane. Without any modification, it can be directly applied to single linear sensors and multi-linear sensors.
375 Robustness of Change Detection Algorithms in the Presence
of Registration Errors
Ashok Sundaresan, Pramod K. Varshney, and Manoj K. Arora
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Accurate registration of multi-temporal remote sensing
images is critical to any change detection study. The
presence of registration errors in the images may affect the
accuracy of change detection. In this paper, we evaluate the
performance of two change detection algorithms in the
presence of artificially introduced registration errors in the
dataset. The algorithms considered are image differencing
and an algorithm based on a Markov random field (MRF)
model. Registration errors have been introduced in four
different ways: only in x direction, only in y direction, in
both x and y directions without any rotational misregistration, and finally in both x and y directions together with
rotational misregistration. Three temporal datasets, a
simulated dataset and two synthetic datasets created from
remote sensing images acquired by the Landsat TM sensor,
have been used in our study. The results indicate that the
change detection algorithm based on the MRF model is more
robust to the presence of registration errors than the image
differencing method.
385 Improvement of Lidar Data Accuracy Using Lidar-Specific Ground Targets
Nora Csanyi and Charles K. Toth
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With recent advances of lidar technology, the accuracy
potential of lidar data has significantly improved. State-of-the-art lidar systems can achieve 2 to 3 cm ranging accuracy
under ideal conditions, which is the accuracy level required
by engineering scale mapping. However, this is also the
accuracy range that cannot be realized by routine navigation-based direct sensor platform orientation. Furthermore,
lidar systems are highly integrated multi-sensor systems, and
the various components, as well as their spatial relationships, introduce different errors that can degrade the lidar
data accuracy. Even after careful system calibration, including individual sensor calibration and sensors intra-calibration, certain errors in the collected data can still be present.
These errors are usually dominated by navigation errors and
cannot be totally eliminated without introducing absolute
control information into the lidar data. Therefore, to support
applications that require extremely high, engineering scale
mapping accuracy, such as transportation corridor mapping,
we propose the use of lidar-specific ground targets. Simulations were performed to determine the most advantageous
lidar target design and targets were fabricated based upon
the simulation results. To investigate the potential of using
control targets for lidar data refinement, test flights were
carried out with different flight parameters and target
distributions. This paper provides a description of the
optimal lidar target design, the target identification algorithm, and a detailed performance analysis, including the
investigation of the achievable lidar data accuracy improvement using lidar-specific ground control targets in the case
of various target distributions and flight parameters.
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397 Estimating Nitrogen in Eucalypt Foliage by Automatically
Extracting Tree Spectra from HyMap™ Data
Zhi Huang, Xiuping Jia, Brian J. Turner, Stephen J. Dury, Ian R. Wallis, and William J. Foley
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Airborne HyMap™ data obtained from the crown reflectance
of Eucalyptus melliodora were used to estimate nitrogen in
the foliage. Estimating chemical concentrations in individual
crowns by remote sensing is especially difficult for eucalypts
because, first, there is marked variation between individual
crowns and, secondly, separating leaf and background
spectral information is difficult. We developed an automatic
method to select relatively pure tree pixels for each tree. In
this method, the background materials are modeled, and the
pixels within a crown that do not resemble the background
clusters are regarded as target pixels. A modified partial
least squares gave an R2 value for predicted versus determined
nitrogen concentrations of 0.79, with an RMSE of 0.69 mg/g,
less than half the standard deviation of the measured
values. Automatically selecting tree pixels was more accurate than manual selection, while the study confirmed that
using the maximum spectrum gives results that are as
accurate as those from the mean spectrum.
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403 Generation of Orthoimages and Perspective Views
with Automatic Visibility Checking and Texture Blending
George E. Karras, Lazaros Grammatikopoulos,
Ilias Kalisperakis, and Elli Pets
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Conventional orthorectification software cannot handle
surface occlusions and image visibility. The approach
presented here synthesizes related work in photogrammetry
and computer graphics/vision to automatically produce
orthographic and perspective views based on fully 3D
surface data (supplied by laser scanning). Surface occlusions
in the direction of projection are detected to create the
depth map of the new image. This information allows
identifying, by visibility checking through back-projection of
surface triangles, all source images which are entitled to
contribute color to each pixel of the novel image. Weighted
texture blending allows regulating the local radiometric
contribution of each source image involved, while outlying
color values are automatically discarded with a basic
statistical test. Experimental results from a close-range
project indicate that this fusion of laser scanning with multi-view photogrammetry could indeed combine geometric
accuracy with high visual quality and speed. A discussion of
intended improvements of the algorithm is also included.
413 Improving Land-cover Classification Using Recognition
Threshold Neural Networks
M.J. Aitkenhead and R. Dyer
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The use of neural networks to classify land-cover from
remote sensing imagery relies on the ability to determine a
winner from the candidate land-cover types based on the
imagery information available. In the case of a “winner-
takes-all” scenario, this does not allow us a measure of
how much the prediction of each pixel’s land-cover can
be trusted. We present a three-stage method where only
winning candidates which are given a clear lead over the
other land-cover types are accepted, with a neighborhood
relationship and the application of mixed pixels being used
to provide full classification. This method allows us to place
more faith in the resulting map than simply taking the
winner, and results in a higher accuracy of classification.
The method is applied to Landsat imagery of an area of the
Philippines where natural, urban, and cultivated land-cover
types exist.