Peer-Reviewed Articles
531 Automated Feature Generation in Large-Scale
Geospatial Libraries for Content-Based Indexing
Kenneth W. Tobin, Budhendra L. Bhaduri, Eddie A. Bright,
Anil Cheriyadat, Thomas P. Karnowski, Paul J. Palathingal,
Thomas E. Potok, and Jeffery R. Price
Abstract Download Full Article
We describe a method for indexing and retrieving high resolution
image regions in large geospatial data libraries.
An automated feature extraction method is used that
generates a unique and specific structural description
of each segment of a tessellated input image file. These
tessellated regions are then merged into similar groups, or
sub-regions, and indexed to provide flexible and varied
retrieval in a query-by-example environment. The methods
of tessellation, feature extraction, sub-region clustering,
indexing, and retrieval are described and demonstrated
using a geospatial library representing a 153 km2 region
of land in East Tennessee at 0.5 m per pixel resolution.
541 Semiautomatic Building Line Extraction from Ikonos
Images Through Monoscopic Line Analysis
Taejung Kim, Tae-Yoon Lee, and Kyung-Ok Kim
Abstract Download Full Article
This paper proposes a new algorithm for extracting building
lines from monoscopic high-resolution satellite images. We
focus on extracting lines from rectangular-shaped building
roofs with a relatively large size. We achieve this task by
devising a unique approach using line voting and matching.
Our algorithm works as follows. An input point on a building
roof is given manually. A region of interest is defined centered
on the input point and within the region lines extracted. Then,
a line voting process is applied to estimate initial orientation
and position of a building line. The orientation and position
are refined by a least squares matching process. We assessed
the performance of our algorithm against two Ikonos images.
Our algorithm extracted building lines from over 83 percent of
buildings tested with average angular accuracy of one or two
degrees and average positional accuracy of approximately one
pixel. This result supports the algorithm proposed in this
paper. The major contribution of this paper is that we provided
an alternative way to line grouping approaches when
edge responses from buildings are not strong enough.
551 Surface Mapping Using Image Triplets: Case Studies
and Benefi t Assessment in Comparison to Stereo
Image Processing
Hannes Raggam
Abstract Download Full Article
Mapping of the Earth’s surface from satellite images is a
continuing application in remote sensing, which has been
distinctly pushed with the increasing availability of very
high-resolution image data. In most of the missions, stereo
images can be acquired making 3D mapping from space very
attractive, as high accuracy and level of detail become
feasible from such data. In this paper, several case studies
are presented, which have been applied to stereo pairs
acquired by the SPOT sensors as well as the very highresolution
Ikonos sensor. As an extension to standard stereo
mapping, a 3D mapping approach which makes use of
multiple image coverage is presented. For this work, image
triplets including multi-sensor data sets have been used to
extract 3D topography information and to generate digital
surface models. The benefit of this extended approach is
comparatively assessed with respect to results achieved from
standard stereo mapping.
565 High-resolution Image Fusion: Methods to Preserve
Spectral and Spatial Resolution
Andreja Švab and Krištof Ošti
Abstract Download Full Article
The main topic of this paper is high-resolution image
fusion. The techniques used to merge high spatial resolution
panchromatic images with high spectral resolution
multispectral images are described. The most commonly
used image fusion methods that work on the principle of
component substitution (intensity-hue-saturation method
(IHS), Brovey transform (BT), and multiplicative method
(MULTI)) have been applied to Ikonos, QuickBird, Landsat,
and aerial orthophoto images. Visual comparison, histogram
analyses, correlation coefficients, and difference
images were used in order to analyze the spectral and
spatial qualities of the fused images. It was discovered that
for preserving spectral characteristics, one needs a high
level of similarity between the panchromatic image and the
respective multispectral intensity. In order to achieve this,
spectral sensitivity of multispectral and panchromatic data
was performed, and digital values in individual bands have
been modified before fusion. It has also been determined
that spatial resolution is best preserved in the event of an
unchanged input panchromatic image.
573 The Geometrical Comparisons of RSM and RFM for
FORMOSAT-2 Satellite Images
Liang-Chien Chen, Tee-Ann Teo, and Chien-Liang Liu
Abstract Download Full Article
In this paper, we compare the geometrical performance
between the rigorous sensor model (RSM) and rational
function model (RFM) in the sensor modeling of FORMOSAT-2
satellite images. For the RSM, we provide a least squares
collocation procedure to determine the precise orbits. As
for the RFM, we analyze the model errors when a large
amount of quasi-control points, which are derived from the
satellite ephemeris and attitude data, are employed. The
model errors with respect to the length of the image strip
are also demonstrated. Experimental results show that the
RFM is well behaved, indicating that its positioning errors is
similar to that of the RSM.
581 A Bottom-up Approach to Vegetation Mapping of the
Lake Tahoe Basin Using Hyperspatial Image Analysis
Jonathan A. Greenberg, Solomon Z. Dobrowski, Carlos M.
Ramirez, Jahale L. Tuil, and Susan L. Ustin
Abstract Download Full Article
Increasing demands on the accuracy and thematic resolution
of vegetation community maps from remote sensing
imagery has created a need for novel image analysis techniques.
We present a case study for vegetation mapping of
the Lake Tahoe Basin which fulfills many of the requirements
of the Federal Geographic Data Committee base-level
mapping (FGDC, 1997) by using hyperspatial Ikonos imagery
analyzed with a fusion of pixel-based species classification,
automated image segmentation techniques to define vegetation
patch boundaries, and vegetation community classification
using querying of the species classification raster
based on existing and novel rulesets. This technique led to
accurate FGDC physiognomic classes. Floristic classes such
as dominance type remain somewhat problematic due to
inaccurate species classification results. Vegetation, tree
and shrub cover estimates (FGDC required attributes) were
determined accurately. We discuss strategies and challenges
to vegetation community mapping in the context of standards
currently being advanced for thematic attributes and
accuracy requirements.
591 MTF-tailored Multiscale Fusion of High-resolution MS
and Pan Imagery
B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, and M. Selva
Abstract Download Full Article
This work presents a multiresolution framework for merging
a multispectral image having an arbitrary number of
bands with a higher-resolution panchromatic observation.
The fusion method relies on the generalized Laplacian
pyramid (GLP), which is a multiscale, oversampled structure.
The goal is to selectively perform injection of spatial
frequencies from an image to another with the constraint of
thoroughly retaining the spectral information of the coarser
data. The novel idea is that a model of the modulation
transfer functions (MTF) of the multispectral scanner is
exploited to design the GLP reduction filter. Thus, the
interband structure model (IBSM), which is calculated at the
coarser scale, where both MS and PAN data are available,
can be extended to the finer scale, without the drawback
of the poor enhancement occurring when MTFs are assumed
to be ideal filters. Experiments carried out on QuickBird
data demonstrate that a superior spatial enhancement,
besides the spectral quality typical of injection methods,
is achieved by means of the MTF-adjusted fusion.
597 Comparison of 3D Physical and Empirical Models for
Generating DSMs from Stereo HR Images
Thierry Toutin
Abstract Download Full Article
This research study addressed and compared 3D physical
and empirical models for stereo-processing and the generation
of digital surface models (DSMs) from different stereo highresolution
(HR) sensors (Ikonos and QuickBird). The 3D
physical model is Toutin’s Model (TM) developed at the
Canada Centre for Remote Sensing, and the empirical model
is the rational function model (RFM). The study also evaluated
the conditions of experimentation to appropriately use these
3D models. The first results on stereo-bundle adjustments
demonstrated that TM and vendor-supplied RFMs gave similar
results with Ikonos as soon as RFM was refined with a shift
computed from one GCP. On the other hand, TM gave better
results than vendor-supplied RFMs with QuickBird regardless
of the polynomial order and the number of GCPs. Due to its
relief dependency, QuickBird RFM needed to be refined at
least with linear functions computed from at least 6 to 10
GCPs. Some large errors were, however, noted on forward
image RFM in column. The DSMs were then generated using an
intensity matching approach and compared to 0.2 m accurate
lidar elevation data. Because DSMs included the height of
land-cover (trees, houses), elevation linear errors with 90
percent confidence level (LE90) were computed and compared
for the entire area and three land-cover classes (forests, urban/
residential, bare surfaces). TM and vendor-supplied RFMs with
Ikonos, regardless of the method and GCP number, achieved
comparable results for all classes, while TM achieved overall
better results than vendor-supplied RFMs with QuickBird. All
results demonstrated the necessity of refining Ikonos RFM with
a shift and one GCP only and QuickBird RFM with 1st-order
linear functions and 6 to 10 GCPs due to its relief dependency.