VOID FILL OF SRTM ELEVATION DATA
- PRINCIPLES, PROCESSES AND PERFORMANCE
Steve Dowding, Director, NEXTMap Products Division
Trina Kuuskivi, SRTM Quality Manager
Xiaopeng Li, Ph.D., Mapping Scientist
Intermap Technologies Corp.
2 Gurdwara Road, Suite 200
Ottawa, Ontario, Canada K2E 1A2
[email protected]
[email protected]
[email protected]
ABSTRACT
The Shuttle Radar Topography Mission (SRTM), flown in February 2000, acquired radar data covering
approximately 80 percent of the Earth's landmass. These data were used in producing a dataset of SRTM Digital
Terrain Elevation Data (DTED®). Given the complex nature of IFSAR technology combined with the sensitive
interaction of radar energy with the atmosphere and ground targets, the resulting dataset contained voids. During the
initial finishing process of SRTM elevation data, many small voids were filled when water bodies were flattened.
However, some areas of void still remained in the finished SRTM DTED®. The National Geospatial-Intelligence
Agency (NGA) desires a fully populated SRTM DTED® dataset over priority areas identified by its customers.
NGA has contracted the SRTM Boeing-Intermap team to develop and utilize a capability to detect and remove
phase unwrapping errors and to fill SRTM DTED® voids with alternate source DEMs. This paper focuses on the
principles and processes used in the void fill program by the SRTM Boeing-Intermap team. Results of a preliminary
evaluation of the void filled SRTM data show the effectiveness of the implemented void fill process.
INTRODUCTION
The Shuttle Radar Topography Mission (SRTM), flown in February 2000, utilized a single-pass, across-track
Interferometric Synthetic Aperture Radar (IFSAR) to collect X-band and C-band IFSAR data over 80 percent of the
landmass of the Earth between 60o north and 56o south latitude (Figure 1). The mission was co-sponsored by the
National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA).
Its objective was to obtain the most complete, near-global, homogeneous, high-resolution dataset of the Earth’s
topography ever produced (Chien, 2000), which would benefit geospatial data users around the world.
NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of the raw C-band SRTM data and
forwarded partially finished data directly to NGA for finishing by NGA's contractors – BAE and Boeing. Intermap
Technologies teamed with Boeing in the development of a system for data finishing, and the use of that system to
perform the production processing of SRTM DTED® Level 2 products (Noble et al., 2003). The production
includes detecting and correcting spikes and wells, detecting and flattening water bodies, stepping down rivers and
delineating shorelines. Nevertheless, due to the complex nature of IFSAR technology combined with the sensitive
interaction of radar energy with the atmosphere and ground targets, the data contained a number of voids. Many
small voids were filled when water bodies were flattened during the production process of SRTM data, but some
still remained in the finished SRTM DTED®.
Geospatial applications typically desire a fully populated elevation dataset. Elevation datasets containing voids
have limited application value and are therefore less useful. Many users and researchers have developed their own
ways to fill voids in the publicly available SRTM dataset but due to the complexity of the voids and lack of
appropriate source data for filling, satisfactory results are hard to achieve.
NGA has contracted the SRTM Boeing-Intermap team to develop an automated capability to detect and fill
voids in the SRTM DTED® data with source DEMs such as NGA-supplied DTED®. More recently, NGA further
contracted the Boeing-Intermap team to perform an end-to-end SRTM void fill program over priority areas
identified by its customers using the developed capability.
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The objective of this paper is to describe the principles, processes and performance of the void fill program.
After a brief introduction to the SRTM DTED® products, the causes of the voids will be explained. Implementation
of the void fill program is then discussed along with results of a preliminary evaluation of the filled SRTM data.
Figure 1. SRTM Final Coverage Map: Flat Map (JPL, 2003)
SRTM DATA FINISHING AND DTED® PRODUCTS
After JPL’s preliminary data processing on a continental basis, NGA conducted quality assurance and data
finishing through its contractors to produce SRTM DTED® Level 2 products.
During the data finishing process of the SRTM DTED® Level 2 products, the following tasks were
implemented:
w Spikes and wells in the data were detected and voided out if they exceeded 100 meters compared to
surrounding elevations.
w Small voids (16 contiguous posts or less) were filled by interpolation of surrounding elevations. Large
voids were left in the data.
w Water bodies were edited. The ocean elevation was set to 0 meter. Lakes of 600 meters or more in length
were flattened and set to a constant height. Rivers that exceeded 183 meters in width were delineated and
monotonically stepped down in height.
w Islands were depicted if they had a major axis exceeding 300 meters or if the relief was greater than 15
meters above the surrounding water elevation.
SRTM DTED® Level 1 products were then derived from the finished SRTM DTED® Level 2 products. The
finished SRTM DTED® products that are designated public are distributed through the United States Geological
Survey’s EROS Data Center. The SRTM DTED® Level 2 products are only publicly available for United States
while the Level 1 products are available for the entire world. Table 1 describes the major specifications of SRTM
DTED® products.
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Table 1. Specifications of SRTM DTED® Products*
Items
Specifications
Extent of product unit
Horizontal resolution
(latitude x longitude)
1o x 1o (latitude x longitude)
Level 1: 3’ x 3’ (0o to 50o latitude), 3’ x 6’ (50o to 60o latitude)
Level 2: 1’ x 1’ (0o to 50o latitude), 1’ x 2’ (50o to 60o latitude)
1m
Level 1: 1201 x 1201 (0o to 50o latitude)
1201 x 601 (50o to 60o latitude)
Level 2: 3601 x 3601 (0o to 50o latitude)
3601 x 1801 (50o to 60o latitude)
WGS84
Mean sea level defined by EGM 96 geoid
16 m LE90 (absolute)
Vertical precision
Dimension (posts)
Horizontal datum
Vertical datum
Vertical accuracy
Note
Edge matched
Derived from the Level 2 product
All elevations are in integer meters
* This table is compiled from JPL (2003) and USGS (2004).
CAUSES OF VOIDS IN SRTM DATA
SRTM collected data during both ascending and descending passes. The hope was that with the increased
coverage there would be fewer voids in the final product. On the whole, the data are approximately 95% complete
over the collection area. However, SRTM products are IFSAR-derived and some of the data may exhibit typical
radar artifacts including scattered voids due to shadow and layover effects or poor signal returns over some terrain,
and occasional phase unwrapping errors. Figure 2 illustrates a partial SRTM DTED® product with voids in the
mountainous areas. The following briefly discusses the typical causes of the voids in the SRTM data.
Figure 2. Voids in SRTM DTED® Data (white pixels)
Geometric Artifacts
Geometric artifacts, such as foreshortening, layover and radar shadow, are caused by IFSAR systems’ sidelooking nature and the interaction with ground targets. Foreshortening is a tendency for an object to look shorter on
the radar image than it really is. Layover is a severe type of foreshortening, when the top of an object is imaged
before the bottom. Shadow occurs when an area is not illuminated by the radar signals (Intermap, 2003). These
phenomena cause correlation between the two interferometric channels to drop producing a data loss in the affected
region. Having a number of SRTM passes has reduced (although not completely eliminated) the amount of these
geometric artifacts in SRTM data.
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Specular Reflection of Water
Water causes a specular reflection of radar signals. Water acts like a mirror with most of the radar signals
reflected away from the sensor. Most water areas in raw SRTM data are either voids or very “bumpy” in appearance
and have been flattened in the initial SRTM production. However, there are areas where water containing voids was
either too small to be edited or phase unwrapping errors occurred that prevented water from being completely
finished.
Phase Unwrapping Artifacts
The signal returned to an IFSAR system contains both phase and magnitude information. Phase information is
used to produce the DEMs through an interferometric process. To convert the phase information to an elevation,
phase unwrapping is needed. In areas where the phase cannot be unwrapped correctly because of layover or water
issues etc., the algorithm will leave voids in the DEM. Often, certain seed data are used to help correct phase
unwrapping. However, if there is no seed data at the beginning of a data strip unwrapping might not be started
properly.
Complex Dielectric Constant (CDC)
The SRTM data in desert regions contained voids because of the effect of the Complex Dielectric Constant
(CDC). CDC influences the ability of a surface to absorb, reflect and transmit microwave energy. Surfaces with
high CDC (e.g. 80 for water) are excellent reflectors of energy. Surfaces with lower CDC value imply more
absorption of energy and, thus, penetration beneath the surface (Intermap 1997). Since deserts are very dry, the
IFSAR energy penetrates the sand and unless there is something for it to interact with; little or no energy will be
returned to the IFSAR sensor.
BOEING-INTERMAP SRTM VOID FILL PROCESS
NGA has contracted the SRTM Boeing-Intermap team to develop and utilize a capability to identify and fill the
voids in SRTM DTED® products using appropriate source DEMs supplied by NGA. At the moment of preparing
this paper, the full-scale void fill production has started. The primary purpose of the void fill project is to fill the
residual voids that were left in the SRTM DTED® Level 2 product. Typically, only cells containing less than 10%
void are filled using the highest resolution DEM available. Primarily existing DTED® Level 2 is used and when
not available a densified DTED® Level 1 is used. In addition, SRTM DTED® Level 2 may also contain anomalous
elevation data due to cycle shifts during phase unwrapping. Cycle shifts usually occur in areas with low signal-tonoise ratio, high relief and where insufficient or inaccurate ground truth is available.
The following briefly describes Boeing-Intermap’s void fill (VF) process. Figure 3 shows the process
flowchart.
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Figure 3. Process Flowchart of the Void Fill
Data Ingesting and Screening
Finished SRTM DTED® Level 2, Terrain Height Error Data (THED), Ortho-rectified Image Mosaics (OIM),
SRTM Water Body Data, (SWBD), Alternate Source DEM (ASDEM) and Government Furnished Information
(GFI) source DEMs are ingested and screened by the SRTM VF software on a cell basis. Ingesting is conducted to
check for completeness, convert the data into proper format and place them into the designated directories. The
automatic screening process is run on the GFI source DEMs which consists of NGA’s worldwide holding of
DTED® Level 2, DTED® Level 1 and National Elevation Data (NED). The screening process calculates the
horizontal and vertical shift and accuracy level of each DEM. SRTM input data are verified, and statistics calculated
for the Void Fill Feasibility Tool (VFFT). Void percentage is calculated to ensure compliance with specification.
During the data ingest and screening phase, a water mask and a shapefile of SRTM voids are also generated for the
VFFT. All edges are checked to ensure they all match.
Automatic Phase Unwrap Error Detection (PUED)
The Auto-PUED is run at two different stages: once to produce information for the VFFT and a second time for
PUED production. Successful PUED requires an accurate reference DEM source. Reference DEM anomalies/error
can create false positives that require additional operator analysis. The PUED is run on a composite cell using
adjacent cells. PUED compares SRTM DTED® Level 2 to the primary source DEM. A PUE (Phase Unwrap Error)
post is defined as the elevation of any SRTM post that is more than 200 m different than the source DEM being
compared to. A PUE region can then be expanded to any set of posts connecting to the “PUE Point” that is more
that 100 m different than the source DEM. A shapefile is then created of the Phase Unwrap Error Candidates
(PUEC) regions for the VFFT.
Void Fill Feasibility Tool (VFFT)
The VFFT is a web-based reporting tool that produces reports on acceptability of GFI DEM and ASDEM. It
also has the following functionalities:
w To provide coverage availability of GFI DEM and ASDEM.
w To produce statistics for void fill production.
w To display void, PUEC and water information for each cell.
Once all the statistics are displayed NGA will review and determine which cells will be edited and with what
source DEM.
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PUED Interactive Edit
Auto-PUED is re-run with new information from the VFFT. PUEC are produced and interactively reviewed by
an editor within VF software environment to determine if the candidates are valid. The editor will either accept or
delete each PUEC based on the PUEC’s validity, as determined by training and experience.
Automated Pre-Void and Automated Void Fill
The automated Pre-Void process sets all valid PUEC areas to void and the central region of the cell is filled
with either GFI or ASDEMs. During the Automated Void Fill process voids are filled with either GFI source DEM
or Alternate Source DEMs when available. Regions surrounding the voids are feathered for a smoother transition
into SRTM data. All adjacent cells need to be pre-processed before Automated Void fill will run.
Void Fill Editing and Water Finishing
After the void filling process, the editor uses several tools and sources to inspect and interactively validate the
filled areas. Void-filled regions adjacent to water will be identified and these water features will be edited according
to the SRTM Edit Rules.
Quality Assurance (QA), Data Finishing, Independent Verification and Validation (IV&V)
After the completion of Void Fill Editing and Water Finishing, an interactive QA will be conducted for each
cell to assure that all editing is consistent and follows the SRTM Water Edit Rules. After a cell has passed through
the Interactive QA, automated Data Finishing will be carried out which updates the THED and other related files
according to the changes made to the SRTM DTED® Level 2. An SRTM DTED® Level 1 and SWBD are
generated from the DTED® Level 2 file data and integrity checks will automatically occur on each SRTM data file.
In addition, edges and water elevations are checked. No voids shall remain in the filled SRTM DTED® Level 2
products. Once the files have completed the Data Finishing process, the cells will undergo the IV&V process before
they are delivered to NGA.
PRELIMINARY EVALUATION OF VOID FILLED SRTM DATA
The following is a preliminary evaluation of the of the void filled SRTM data.
SRTM Finished Data and Void-Filled Data
Two finished SRTM DTED® Level 2 cells containing voids (in different continents with different terrain
relief) were selected for the void fill evaluation. Table 2 lists the main characteristics of the cells. All the voids in
these two cells were filled with appropriate source data using the above-described NGA-approved Boeing-Intermap
void fill procedures. Figures 4 and 5 illustrate the two cells before and after the void fill.
Terrain
relief
Table 2. Characteristics of Evaluation Cells
Void
Descriptions
percentage
Cell 1
4 ~ 2657 m
2.6%
Mountainous in the north and rolling/flat in the south. Contains water bodies.
Cell 2
139 ~ 4412 m
1.3%
Mountainous in west and rolling/flat in the east. Contains water bodies.
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(a) Before Void Fill
(b) After Void Fill
Figure 4. SRTM DTED® Cell 1
(a) Before Void Fill
(b) After Void Fill
Figure 5. SRTM DTED® Cell 2
Reference Dataset
Two different reference datasets were used in the evaluation for each cell. One set contains fully populated
DEMs with the same resolution, datum and projection covering either all or part of the SRTM cell (Figure 6).
Reference data for Cell 1 one was best available at the time. The second reference dataset is a select number of
ground check points (GCPs). Figure 7 shows the GCP distribution in the two cells. Table 3 summarizes the main
characteristics of the reference dataset.
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Table 3. Reference Dataset used in the Void Fill Evaluation
Cell 1
Cell 2
Reference DEM
Ground check points
Reference DEM
Ground check points
Coverage
Whole cell
Uniform through the whole
cell
Source
NGA provided
Alternate Source DEM
NGA provided GFI.
Vertical
accuracy
(LE90)
10 m (slope < 20)
18 m (slope < 40)
30 m (slope > 40)
8m
Only covers part of the
cell
Intermap IFSAR DEM
data resampled from 5 m
to 1 arc second resolution
2m
East part only
Downloaded from the
National Geodetic
Survey (NGS) website
Geodetic quality
SRTM DTED® cell
coverage (Cell 2)
Reference
DEM
coverage
Figure 6. Reference DEM Coverage (delineated by blue lines) for Cell 2
Figure 7. GCP (black dots) Distribution for both Cells
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Evaluation Procedures
Void Selection. In the preliminary evaluation only seven voids were selected (mainly for visual analysis) to
reduce the amount of interactive effort. These voids were clearly defined with different shapes, sizes and types of
terrain. Table 4 describes the main characteristics of the voids selected for visual evaluation. Figure 8 shows some
of the selected voids.
Table 4. Voids Selected for Visual Analysis
Cell 1
Cell 2
Void
Relief (m)
Size (pixels)
Slope (o)
Relief (m)
Size (pixels)
Slope (o)
1
2
3
223 ~ 462
84 ~ 90
249 ~ 618
281
121
2541
28
1
26
2333 ~ 2551
3217 ~ 3396
3272 ~ 3408
91
66
36
34
43
35
4
482 ~ 629
157
24
2890 ~ 3178
162
41
5
6
7
876 ~ 1747
1163 ~ 1672
1355 ~1599
1699
574
121
42
37
39
3005 ~ 3107
2014 ~ 2379
3313 ~3458
66
72
66
16
44
34
Visual Evaluation is to check the internal consistency of the filled areas (using external non-SRTM data) with
the surrounding valid SRTM data. It is expected that no obvious artifacts exist along the void circumference. Visual
evaluation is mainly conducted using visual check means, such as profiles, color-coded shaded relief, contours, etc.
around the void areas.
Statistical Evaluation is to analyze whether the statistical vertical accuracy of the filled SRTM data meets
specification (16m LE90) based on the GCPs and the difference images between the reference DEMs and the filled
SRTM data.
Figure 8. Examples of Voids
(Green masks indicate feathering posts and brown masks indicate void filled posts)
Results and Analysis
Visual Evaluation is conducted for each selected void in both cells. Various visual aids (shaded relief, profiles
and contours) clearly show that the internal consistency of the filled SRTM data is satisfactory ─ no edge
discontinuity can be found along the void edges which is largely attributed to the appropriate source data for void
fill and the featuring functionality in the filling process. Most filled voids look natural using different visual
checking tools. Figures 9 to 11 are some screen captures of the filled areas with different presentation means.
Statistical Analysis based on GCPs. Tables 5a and 5b summarize the statistical accuracy of various DEM
datasets using GCPs as reference. Figure 12 is the graphic presentation of the results. The Source DEM has been
used to fill the SRTM data voids and the Reference DEM is used for the evaluation.
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Table 5a. Vertical Accuracy Evaluation using GCPs (Cell 1) (Units: meters)
Number of
GCPs: 173
Mean
Max
Min
RMSE
LE90
Unfilled SRTM
Source DEM
Reference
DEM
Filled SRTM
-0.8
10.5
-14.2
3.4
5.6
0.3
9.0
-27.7
3.0
4.9
6.4
22.6
-6.5
7.5
12.4
-0.8
10.5
-24.7
3.8
6.3
Table 5b. Vertical Accuracy Evaluation using GCPs (Cell 2)* (Units: meters)
Number of
Unfilled SRTM
Source DEM
Filled SRTM
GCPs: 75
Mean
-0.7
-2.1
-0.7
Max
10.5
5.5
10.5
Min
-9.6
-13.6
-9.6
RMSE
3.6
3.8
3.6
LE90
5.8
6.3
5.8
* None of GCPs for Cell 2 is within the reference DEM coverage.
Figure 9. Part of Void Filled SRTM Data (green polygons delineate the filled areas)
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Blue: Filled SRTM data
Green: Source data
Red: Reference data
(a) Profiles across a Filled Void
(b) Shaded Relief
(c) Contour Line (25m interval)
Figure 10. Illustrations of A Filled Void in Cell 1 (green polygons delineate the filled areas)
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Blue: Filled SRTM data
Green: Source data
Red: Reference data
(a) Profiles across a Filled Void
Void Area
(b) 3D View
(c) Contour Line (25m interval)
18
16
14
SRTM Vertical Accuracy Target
12
10
8
6
4
2
0
Unfilled SRTM Source DEM
(a) Cell 1
Reference
DEM
Filled SRTM
Vertical Accuracy LE90 (m)
Vertical Accuracy LE90 (m)
Figure 11. Illustrations of A Filled Void in Cell 2
18
16
14
SRTM Vertical Accuracy Target
12
10
8
6
4
2
0
Unfilled SRTM
Source DEM
(b) Cell 2
Figure 12. LE90 Vertical Accuracy of Two Cells using GCPs
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Filled SRTM
The following observations can be made:
w All evaluated DEMs, including unfilled SRTM DTED® Level 2, source DEM, reference DEM (for
Cell 1 only) and the final filled DEM are all within the 16m LE90 specification using the available
GCPs.
w Since the void percentage is relatively small for those two cells (2.6% and 1.3%), statistics are very
similar before and after the void fill. However, minor differences can be found since the alternative
data is subject to some horizontal and vertical shifts depending on the agreement level between the
source data and surrounding SRTM data.
w For Cell 1, the reference DEM has a vertical bias compared with all other datasets.
w Inaccuracy in the source DEM in the void areas will definitely be transferred to the filled data if no
other better source DEM is available to fill the voids. This is proven by slight accuracy degradation in
Cell 1 after the void fill (see the red numbers).
Statistical Analysis based on the difference images. Table 6 gives statistics of the difference images.
Tables 7a and 7b summarize statistics associated with each of the seven voids selected for visual analysis.
Table 6. Statistics of Difference Images
Cell 1
Difference
range (m)
Standard
deviation
8
-738 ~ 732
29
Non-voids
area
8
-667 ~ 732
22
Voids only
10
-738 ~ 695
114
7 selected
voids
-16
-333 ~135
60
All area
7
-744 ~ 730
29
Voids only
6
-744 ~ 692
114
7 selected
voids
-15
-329 ~135
59
Difference image
Reference DEMFilled SRTM
All area
Mean
(m)
Reference Source
Reference Source
Reference DEMFilled SRTM
Difference image
Cell 2
Mean
(m)
Difference
range (m)
Standard
deviation
All area
-1
-291 ~390
8
Non-voids
area
-3
-245 ~390
14
Voids only
3
-291 ~377
63
7 selected
voids
-3
-64 ~118
24
All area
2
-228 ~391
9
Voids only
6
-288 ~ 380
63
7 selected
voids
1
-57 ~130
23
Table 7a. Statistics of Each Individual Void (Cell 1)
Reference-Source
Reference-SRTM Filled
Difference
Standard
Mean
Difference
Standard
Mean (m)
range (m)
deviation
(m)
range (m)
deviation
-1
-34 ~ 25
9
0
-33 ~ 26
9
4
3~7
1
4
3~9
1
11
-112 ~135
24
25
-112 ~ 135
11
Void
Size
(pixel)
1
2
3
281
121
2541
4
157
16
-6 ~ 45
12
19
-3 ~ 48
12
5
6
7
1699
574
121
-62
-12
-9
-329 ~71
-173 ~68
-65 ~18
78
47
16
-66
-5
-32
-333 ~ 70
-166 ~ 77
-86 ~ -10
78
47
16
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Table 7b. Statistics of Each individual void (Cell 2)
Void
Size
(pixel)
Mean (m)
1
2
3
4
5
6
7
91
66
36
162
66
72
66
-9
1
-11
-6
9
39
-9
Reference-Source
Difference
range (m)
-17 ~3
-14 ~16
-19 ~ -1
-57 ~47
-2 ~25
3 ~130
-18 ~17
Standard
deviation
4
7
5
23
5
31
7
Mean
(m)
-18
21
-16
-13
3
28
-16
Reference-SRTM Filled
Difference
Standard
range (m)
deviation
-25 ~ -9
4
2 ~45
9
-40 ~-6
6
-64 ~40
23
-8 ~14
4
-3 ~118
29
-27 ~17
8
In Cell 1 large differences exist between the reference DEM and all other DEM datasets. This is probably due
to the fact that different methodologies, procedures and water body interpretations were used for generating those
DEMs. Furthermore, for some terrain, if it is difficult for one method (e.g. SRTM) to depict accurately, it may also
be problematic for another method (e.g. optical satellite imaging) to do so. For example in Table 7a, Void #5 and #6
and in Table 7b, Void #6 have large mean differences and standard deviations. Both statistical and visual analysis of
all difference images show that the quality of the reference DEMs plays an important role in the analysis. Therefore,
only when more appropriate reference DEMs in terms of accuracy and coverage are available can more observations
and conclusions be made for this cell. Results from Cell 2 are more encouraging since the reference DEM has a
higher fidelity. In Cell 2, general agreement between the reference DEM and the filled data is very good although
there are still some localized large differences.
SUMMARY AND FUTURE WORK
In this paper, the SRTM DTED® void fill program is introduced to readers within the context of the SRTM
Boeing-Intermap void fill production environment. Causes of voids in SRTM data are discussed. A step-by-step
void fill process is outlined. Some preliminary evaluation results of void filled data are also presented.
The Boeing-Intermap SRTM void fill process is effective in meeting the desired goals, based on
preliminary visual and statistical analysis results. However, the statistical analysis is largely limited by the available
reference data. With the recent implementation of the Boeing-Intermap void fill production, further observations,
analysis and reporting are planned and will be presented to the geospatial community. Many applications of terrain
data, for various reasons, require complete data with no "holes" in the terrain. The void filling process described
here specifically addresses this need and at the same time attempts to minimize any degradation of the original
products.
ACKNOWLEDGEMENTS
The authors would like to thank the NGA and Boeing SRTM team for their inputs and comments to this paper.
We also like to acknowledge Bob Richardson and Jennifer Lock of Intermap Technologies for their contributions to
the evaluation.
ASPRS Images to Decision:
Remote Sensing Foundation for GIS Applications
September 12-16, 2004* Kansas City, Missouri
ASPRS-70 years of service to the profession
REFERENCES
Chien, P. (2000). Endeavour maps the world in three dimensions. GeoWorld, 13(4): 32-38.
Intermap Technologies (1997). Interferometric SAR Digital Elevation. Third International Airborne Remote Sensing
Conference and Exhibition. Copenhagen, Denmark, July 6th.
Intermap Technologies (2003). Product handbook and quick start guide.
JPL (2003). http://www2.jpl.nasa.gov/srtm/ (Accessed on May 14, 2004).
Noble, T., A. Englert, T. Kuuskivi, G. Dickson (2003). A geospatial success story: the Boeing Autometric-Intermap
SRTM team. In: Proceedings of Terrain Data: Applications and Visualization – Making the Connection,
October 27-30, North Charleston, SC, USA.
USGS (2004). http://srtm.usgs.gov/ (Access on May 14, 2004).
ASPRS Images to Decision:
Remote Sensing Foundation for GIS Applications
September 12-16, 2004* Kansas City, Missouri
ASPRS-70 years of service to the profession