Peer-Reviewed Articles
289 Validation of the ASTER Instrument Level 1A Scene
Geometry
Hugh H. Kieffer, Kevin F. Mullins, and David J. MacKinnon
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An independent assessment of the Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER) instrument
geometry was undertaken by the U.S. ASTER Team, to
confirm the geometric correction parameters developed and
applied to Level 1A (radiometrically and geometrically raw
with correction parameters appended) ASTER data. The goal
was to evaluate the geometric quality of the ASTER system and
the stability of the Terra spacecraft. ASTER is a 15-band system
containing optical instruments with resolutions from 15- to 90-
meters; all geometrically registered products are ultimately tied
to the 15-meter Visible and Near Infrared (VNIR) sub-system.
Our evaluation process first involved establishing a large
database of Ground Control Points (GCP) in the mid-western
United States; an area with features of an appropriate size for
spacecraft instrument resolutions. We used standard U.S.
Geological Survey (USGS) Digital Orthophoto Quads (DOQs) of
areas in the mid-west to locate accurate GCPs by systematically
identifying road intersections and recording their coordinates.
Elevations for these points were derived from USGS Digital
Elevation Models (DEMs). Road intersections in a swath of nine
contiguous ASTER scenes were then matched to the GCPs,
including terrain correction. We found no significant distortion
in the images; after a simple image offset to absolute position,
the RMS residual of about 200 points per scene was less than
one-half a VNIR pixel. Absolute locations were within 80
meters, with a slow drift of about 10 meters over the entire
530-kilometer swath. Using strictly simultaneous observations
of scenes 370 kilometers apart, we determined a stereo angle
correction of 0.00134 degree with an accuracy of one microradian.
The mid-west GCP field and the techniques used here
should be widely applicable in assessing other spacecraft
instruments having resolutions from 5 to 50-meters.
303 A Generic Model for Along Track Stereo Sensors Using Rigorous Orbit Mechanics Pantelis Michalis and Ian Dowman
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In this paper a generic, rigorous sensor model for high resolution
optical satellite sensors with along-track stereoscopic
capabilities is introduced. The idea is to determine
the orbit of the satellite platform covering the time acquisition
of all images, using satellite photogrammetry in combination
with astrodynamics, trying to find common exterior
orientation parameters for all images directly or indirectly.
As a result, the number of unknown parameters is reduced
and also the correlation between them, thus giving a more
stable solution. Great effort is made in order to define the
essential forces which are involved in the acquisition of the
pushbroom images, according to the needed accuracy and
the data provided. The fundamental assumption is that
Keplerian motion is maintained along the acquisition time
of all the along-track images. Various versions of the model
are developed based on different orbit determinationpropagation
methods. An accuracy assessment is made of
the above different orbit determination-propagation methods.
It is possible to extract the exterior orientation of all
images together directly, without ground control points and
using the metadata information, with acceptable accuracy.
The model is evaluated using SPOT5-HRS imagery with
precision close to pixel size. Moreover, the accuracy of the
along-track model is compared with the accuracy of single
image sensor model.
311 A Comparative Study of Landsat TM and SPOT HRG
Images for Vegetation Classification in the Brazilian
Amazon
Dengsheng Lu, Mateus Batistella, Emilio Moran, and Evaristo
E. de Miranda
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Complex forest structure and abundant tree species in the
moist tropical regions often cause difficulties in classifying
vegetation classes with remotely sensed data. This paper
explores improvement in vegetation classification accuracies
through a comparative study of different image combinations
based on the integration of Landsat Thematic Mapper (TM)
and SPOT High Resolution Geometric (HRG) instrument data,
as well as the combination of spectral signatures and textures.
A maximum likelihood classifier was used to classify
the different image combinations into thematic maps. This
research indicated that data fusion based on HRG multispectral
and panchromatic data slightly improved vegetation
classification accuracies: a 3.1 to 4.6 percent increase in the
kappa coefficient compared with the classification results
based on original HRG or TM multispectral images. A combination
of HRG spectral signatures and two textural images
improved the kappa coefficient by 6.3 percent compared with
pure HRG multispectral images. The textural images based
on entropy or second-moment texture measures with a
window size of 9 pixels 9 pixels played an important role
in improving vegetation classification accuracy. Overall,
optical remote-sensing data are still insufficient for accurate
vegetation classifications in the Amazon basin.
323 Texture Feature Fusion with Neighborhood-Oscillating
Tabu Search for High Resolution Image Classification
Liangpei Zhang, Yindi Zhao, Bo Huang, and Pingxiang Li
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Multi-channel Gabor filters (MGFs) and Markov random
fields (MRFs) are two common methods for texture analysis.
This paper investigates their integration through a novel
algorithm using the neighborhood-oscillating tabu search
(NOTS) for high-resolution image classification. The NOTS
algorithm fuses the texture features extracted by MGF and
MRF. This algorithm has been compared with classical
methods such as sequential forward selection, sequential
forward floating selection, and oscillating search. Experimental
results show that the fused MGF/MRF features have
much higher discrimination than pure features, and NOTS
outperforms other algorithms with either pure or fused
features. The stability and effectiveness of the proposed
algorithm have been verified using Brodatz, Ikonos, and
QuickBird images.
333 Land-cover Classification Using ASTER Multi-band
Combinations Based on Wavelet Fusion and SOM
Neural Network
Hasi Bagan, Qinxue Wang, Masataka Watanabe, Satoshi
Kameyama, and Yuhai Bao
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In this study, we developed a land-cover classification
methodology using Advanced Spaceborne Thermal Emission
and Reflection Radiometer (ASTER) visible near-infrared
(VNIR), shortwave infrared (SWIR), and thermal infrared (TIR)
band combinations based on wavelet fusion and the selforganizing
map (SOM) neural network methods, and compared
the classification accuracies of different combinations
of ASTER multi-band data. A wavelet fusion concept named
ARSIS (Amélioration de la Résolution Spatiale par Injection
de Structures) was used to fuse ASTER data in the preprocessing
stage. In order to apply the wavelet fusion method to
ASTER data, the principal components of ASTER VNIR data
were computed. The first principal component was used as
the base image for wavelet fusion. In our experiments, the
spatial resolution of ASTER VNIR, SWIR, and TIR data was
adjusted to the same 15 m. SOM classification accuracy was
increased from 83 percent to 93 percent by this fusion, and
classification accuracy increased along with the increase of
band numbers. Classification accuracy reaches the highest
value when all 14 bands are used, but classification accuracy
closely approached the highest value when three VNIR bands,
three SWIR bands, and two TIR bands were used. A similar
tendency was also obtained by the maximum likelihood
classification (MLC) method, but the classification accuracies
of MLC over all band combinations were considerably
obviously lower than those obtained by the SOM method.
343 Parametric Investigation of the Performance of Lidar
Filters Using Different Surface Contexts
Suyoung Seo and Charles G. O’Hara
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Lidar technology has provided an accurate and efficient way to
obtain digital elevation models. While digital terrain models
(DTMs) are essential products for three-dimensional spatial
applications, extraction of ground points from a mixture of
ground and non-ground points is not straightforward, and
interactive classification of massive point data sets is prohibitive.
To automate the filtering process, many algorithms have
been proposed and demonstrated to produce satisfactory
results when applied with suitably tuned parameters. For
obtaining quality products using lidar filters, however, not only
to figure out their optimal performance, but also to analyze the
cause and effect relationships between filtering steps and their
effects under variable conditions is important. Hence, this
study examined the performance of three popular surface
models for lidar data filtering: morphological operations,
triangulation, and linear prediction. For the test, consistent
setting of parameters was applied across considerably different
landscape datasets. The strengths and weaknesses of the test
filters were investigated by comparing the metrics of omission
and commission errors and volumetric distortions, and by
observing resulting DTMs and relevant surface profiles.
363 Analysis of Turbid Water Quality Using Airborne
Spectrometer Data with a Numerical Weather
Prediction Model-aided Atmospheric Correction
Jenni Attila , Timo Pyhälahti , Tuula Hannonen, Kari Kallio,
Jouni Pulliainen, Sampsa Koponen, Pekka Härmä, and Karri
Eloheimo
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The effects of an atmospheric correction method for water
quality estimation have been studied and validated for
Airborne Imaging Spectrometer for Applications (AISA) data.
This novel approach uses atmospheric input parameters
from a numerical weather prediction model: HIRLAM (High
Resolution Limited Area Model). The atmospheric correction
method developed by de Haan and Kokke (1996) corrects
the spectrometer data according to the coefficients calculated
using Moderate Resolution Transmittance Code
(MODTRAN) radiative transfer code simulations. The airborne
campaigns were carried out at lake and coastal Case 2 type
water areas between 1996 and 1998. The water quality
interpretation was made using the MERIS satellite instrument
wavelengths. The correction improved most of the water
quality (turbidity, total suspended solids, and Secchi disk
depth) estimates when data from several flight campaigns
were used jointly. The atmospheric correction reduced the
standard deviation of the measurements conducted on
different days. The highest improvement was obtained in
the estimation of turbidity.