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 sdowding@intermaptechnologies.com tkuuskivi@intermaptechnologies.com xli@intermaptechnologies.com 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 60 o north and 56 o 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.
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: Spikes and wells in the data were detected and voided out if they exceeded 100 meters compared to surrounding elevations. Small voids (16 contiguous posts or less) were filled by interpolation of surrounding elevations. Large voids were left in the data. 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. 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.
Table 1. Specifications of SRTM DTED Products* Items Specifications Note Extent of product unit 1 o x 1 o (latitude x longitude) Edge matched Horizontal resolution Level 1: 3 x 3 (0 o to 50 o latitude), 3 x 6 (50 o to 60 o latitude) Derived from the Level 2 product (latitude x longitude) Level 2: 1 x 1 (0 o to 50 o latitude), 1 x 2 (50 o to 60 o latitude) Vertical precision 1 m All elevations are in integer meters Dimension (posts) Level 1: 1201 x 1201 (0 o to 50 o latitude) 1201 x 601 (50 o to 60 o latitude) Level 2: 3601 x 3601 (0 o to 50 o latitude) 3601 x 1801 (50 o to 60 o latitude) Horizontal datum WGS84 Vertical datum Mean sea level defined by EGM 96 geoid Vertical accuracy 16 m LE90 (absolute) * 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.
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.
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: To provide coverage availability of GFI DEM and ASDEM. To produce statistics for void fill production. 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.
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. Table 2. Characteristics of Evaluation Cells Terrain Void Descriptions relief 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.
(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.
Coverage Source Vertical accuracy (LE90) Table 3. Reference Dataset used in the Void Fill Evaluation Cell 1 Cell 2 Reference DEM Ground check points Reference DEM Ground check points Whole cell Uniform through the whole Only covers part of the cell cell East part only NGA provided Alternate Source DEM 10 m (slope < 20) 18 m (slope < 40) 30 m (slope > 40) NGA provided GFI. Intermap IFSAR DEM data resampled from 5 m to 1 arc second resolution Downloaded from the National Geodetic Survey (NGS) website 8 m 2 m 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
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 ) 1 223 ~ 462 281 28 2333 ~ 2551 91 34 2 84 ~ 90 121 1 3217 ~ 3396 66 43 3 249 ~ 618 2541 26 3272 ~ 3408 36 35 4 482 ~ 629 157 24 2890 ~ 3178 162 41 5 876 ~ 1747 1699 42 3005 ~ 3107 66 16 6 1163 ~ 1672 574 37 2014 ~ 2379 72 44 7 1355 ~1599 121 39 3313 ~3458 66 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.
Table 5a. Vertical Accuracy Evaluation using GCPs (Cell 1) (Units: meters) Number of GCPs: 173 Unfilled SRTM Source DEM Reference DEM Filled SRTM Mean -0.8 0.3 6.4-0.8 Max 10.5 9.0 22.6 10.5 Min -14.2-27.7-6.5-24.7 RMSE 3.4 3.0 7.5 3.8 LE90 5.6 4.9 12.4 6.3 Table 5b. Vertical Accuracy Evaluation using GCPs (Cell 2)* (Units: meters) Number of GCPs: 75 Unfilled SRTM Source DEM Filled SRTM 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)
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)
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) Figure 11. Illustrations of A Filled Void in Cell 2 Vertical Accuracy LE90 (m) 18 16 14 12 10 8 6 4 2 0 SRTM Vertical Accuracy Target Unfilled SRTM Source DEM Reference DEM Filled SRTM Vertical Accuracy LE90 (m) 18 16 14 12 10 8 6 4 2 0 SRTM Vertical Accuracy Target Unfilled SRTM Source DEM Filled SRTM (a) Cell 1 (b) Cell 2 Figure 12. LE90 Vertical Accuracy of Two Cells using GCPs
The following observations can be made: 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. 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. For Cell 1, the reference DEM has a vertical bias compared with all other datasets. 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. Difference image Reference DEM- Filled SRTM Reference - Source Mean (m) Table 6. Statistics of Difference Images Cell 1 Cell 2 Difference range (m) Standard deviation Difference image Mean (m) Difference range (m) Standard deviation All area 8-738 ~ 732 29 All area -1-291 ~390 8 Non-voids area 8-667 ~ 732 22 Non-voids area -3-245 ~390 14 Voids only 10-738 ~ 695 114 Voids only 3-291 ~377 63 7 selected voids -16-333 ~135 60 Reference DEM- Filled SRTM 7 selected voids -3-64 ~118 24 All area 7-744 ~ 730 29 All area 2-228 ~391 9 Voids only 6-744 ~ 692 114 Voids only 6-288 ~ 380 63 7 selected voids -15-329 ~135 59 Reference - Source 7 selected voids 1-57 ~130 23 Table 7a. Statistics of Each Individual Void (Cell 1) Void Reference-Source Reference-SRTM Filled Size (pixel) Difference Standard Mean Difference Standard Mean (m) range (m) deviation (m) range (m) deviation 1 281-1 -34 ~ 25 9 0-33 ~ 26 9 2 121 4 3 ~ 7 1 4 3 ~ 9 1 3 2541 11-112 ~135 24 25-112 ~ 135 11 4 157 16-6 ~ 45 12 19-3 ~ 48 12 5 1699-62 -329 ~71 78-66 -333 ~ 70 78 6 574-12 -173 ~68 47-5 -166 ~ 77 47 7 121-9 -65 ~18 16-32 -86 ~ -10 16
Void Size (pixel) Mean (m) Table 7b. Statistics of Each individual void (Cell 2) Reference-Source Reference-SRTM Filled Difference range (m) Standard deviation Mean (m) Difference range (m) Standard deviation 1 91-9 -17 ~3 4-18 -25 ~ -9 4 2 66 1-14 ~16 7 21 2 ~45 9 3 36-11 -19 ~ -1 5-16 -40 ~-6 6 4 162-6 -57 ~47 23-13 -64 ~40 23 5 66 9-2 ~25 5 3-8 ~14 4 6 72 39 3 ~130 31 28-3 ~118 29 7 66-9 -18 ~17 7-16 -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.
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