Home Articles Evaluation of Digital Elevation Models created from different satellite images

Evaluation of Digital Elevation Models created from different satellite images

  

Dr. K.S.Siva Subramanian, Amitabh Singh, Manda Sudhakar
RMSI Private Limited, A – 7 Sector 16, NOIDA 201 301, India
Tel +91 120 251 1102, 251 2101, Fax +91 120 251 1109, 251 0963

Introduction
Creating Digital Elevation Model (DEM) by digitizing contour lines from topographic maps or through stereoscopic semi automated methods from aerial photographs are proven methods. However, DEM generation from satellite stereo image pairs of optical and microwave sensors, is still not a common practice. The DEM generated from satellite stereo pairs have some significant advantages over other sources, viz:

  1. World wide availability of satellite data without any restriction (often available as archived data) as against restricted and non availability of topographical maps and aerial photographs
  2. Large area coverage per scene
  3. Moderately high resolution
  4. Faster processing through sophisticated software and little manual effort
  5. Low processing cost
  6. All weather and day/night image acquisition capabilities (in case of microwave sensors)

Scope
The present work has been undertaken:

  1. In order to evaluate the possibility of using satellite DEMs as an alternative source to conventional methods
  2. To assess the accuracies of such DEM’s using different input GCP (Ground Control Points) sources, like different scale paper maps, USGS DEM etc. The different satellite images used for this study include:
    • IRS 1C
    • SPOT
    • ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) VNIR bands
    • Radarsat 1, standard beam mode data (SGF)

Methodology

Inputs details


* ASTER satellite generates an along track stereo pair where, one image is captured in Nadir view and the other is captured 4 seconds later by a backward looking camera by viewing the same geographic area as seen by the Nadir view camera
** These satellites use across track viewing to generate a stereo pair, i.e., the same geographic area is viewed from two different tracks by tilting the cameras

Software used

  • PCI Geometica OrthoEngine v8.2 and
  • Erdas Imagine v8.4

  

DEM generation process
GCPs and stereo GCPs are collected from reference maps in order to reference the images to ground. Further, TPs (Tie points) are collected to improve matching between the two stereo pairs. The following table gives the number of GCPs and Tie Points used to create a DEM:

Satellite Number of GCPs Number of Stereo GCPs Number of TPs Radarsat 53 35 65 ASTER 25 20 30 IRS-IC 12 10 20 SPOT 35 22 38
Once registered to the same ground area, any along track (in case of SPOT, IRS and Radarsat images) or across track (in case of ASTER images) positional differences are assumed to be due to parallax, which results due to relief. Measured parallax differences on a pixel-by-pixel basis are converted to absolute elevations using trigonometric functions and the orbital data (orbital position, altitude, attitude and the scene center). The computation relies on the inherent parallax between stereo images. An automated image correlation algorithm [1] is used to derive elevations from the parallax, by a set of well located GCPs and tie points (TPs).

The image matching system operates on a reference and a search window. For each position in the search window, a match value is computed from gray level values in the reference window. The match value is computed with the mean normalized cross-correlation coefficient and the sum of mean normalized absolute difference [2]. The correlation window size varies from low resolution (8 pixels) to 32 pixels at the full resolution. Elevation points are extracted at every pixel for the complete stereo pair. The 3-D intersection is performed using the above computed geometric model [3] to convert the pixel coordinates in both images determined in the image matching of the stereo pair to the three dimensional data [4].

The output elevations are not computed for the pixels where the image matching fails to find the corresponding pixel in the reference image, resulting into some failure areas. In case of small and scattered failures the software does interpolate and compute most probable values for them. The DEM thus generated is in raw state and does not contain geo-referencing information. So, the DEM needs to be georeferenced.

Workflow

 
DEM generation process
GCPs and stereo GCPs are collected from reference maps in order to reference the images to ground. Further, TPs (Tie points) are collected to improve matching between the two stereo pairs. The following table gives the number of GCPs and Tie Points used to create a DEM:

Satellite Number of GCPs Number of Stereo GCPs Number of TPs Radarsat 53 35 65 ASTER 25 20 30 IRS-IC 12 10 20 SPOT 35 22 38
Once registered to the same ground area, any along track (in case of SPOT, IRS and Radarsat images) or across track (in case of ASTER images) positional differences are assumed to be due to parallax, which results due to relief. Measured parallax differences on a pixel-by-pixel basis are converted to absolute elevations using trigonometric functions and the orbital data (orbital position, altitude, attitude and the scene center). The computation relies on the inherent parallax between stereo images. An automated image correlation algorithm [1] is used to derive elevations from the parallax, by a set of well located GCPs and tie points (TPs).

The image matching system operates on a reference and a search window. For each position in the search window, a match value is computed from gray level values in the reference window. The match value is computed with the mean normalized cross-correlation coefficient and the sum of mean normalized absolute difference [2]. The correlation window size varies from low resolution (8 pixels) to 32 pixels at the full resolution. Elevation points are extracted at every pixel for the complete stereo pair. The 3-D intersection is performed using the above computed geometric model [3] to convert the pixel coordinates in both images determined in the image matching of the stereo pair to the three dimensional data [4].

The output elevations are not computed for the pixels where the image matching fails to find the corresponding pixel in the reference image, resulting into some failure areas. In case of small and scattered failures the software does interpolate and compute most probable values for them. The DEM thus generated is in raw state and does not contain geo-referencing information. So, the DEM needs to be georeferenced.

Workflow

 

The SPOT derived DEM
The DEM extracted from SPOT stereo images (Fig: 2) contain the elevation range of 1m to 1722m. The DEM created at full resolution (i.e., 10m) has resulted into large areas of failure and hence the same has been regenerated at half resolution (20m), which has shown significant improvement in the output with less than 5% failure area. The resulting DEM was validated with USGS DEM of 30m resolution. At 32m was the value at LE90, and CE90 was at 19.5m having a standard deviation also of 32m.


Fig. 2: A) SPOT PAN Left Image (Clip), B) SPOT PAN Right Image (Clip), C) Digital Elevation Model at 20m resolution and D) DEM (Thematic at 50m interval).



As can be observed in the graphic presentation of results (above) that the SPOT derived DEM shows variable results in different slope/terrain conditions. Consistently low values can be seen in the diagram when compared to USGS DEM (30m resolution).

The IRS 1C derived DEM
The Dehradun area is a mixed terrain of moderate relief of the outer Himalayas and Gangetic plains. The IRS stereo pair (Fig. 3) used for this terrain yielded a minimum elevation of 418m to a maximum of 1475m. The DEM thus obtained, was validated with DEM extracted from 1:50,000 scale, Survey of India topographic maps. The LE 90 was 35m and 18m positional accuracy (CE90). The standard deviation in error this case was 34.5m. In this case also the failure area is less than 5% of the complete area.


Fig. 3: A) IRS-IC PAN Left Image (Clip), B) IRS-IC PAN Right Image (Clip), C) Digital Elevation Model at 25m resolution and D) DEM (Thematic at 50m interval)

The following distance vs elevation graph for IRS Pan derived DEM and topographic map derived DEM shows significantly close values for both of the sources whereas secondary undulations in the terrain are depicted better in the IRS derived DEM.

 

The ASTER derived DEM
The elevation range of the DEM obtained from the ASTER stereo images (Fig. 4) was 1000m to 3696m. The ASTER DEM was validated with the DEM extracted from contours obtained from 1:100,000 scale, Russian topographic maps. The vertical accuracy (LE90) was 30m with 20m positional accuracy (CE90). The standard deviation in the measured error w.r.t. reference DEM was 28m. The failure pixels were significantly less (less than 5%), which probably is because of the near synchronous acquisition of stereo images (difference of 4 seconds only) by the satellite.


Fig. 4: A) ASTER VNIR (nadir), Clip, B) ASTER VNIR (backward looking) Clip, C) Digital Elevation Model at 15m resolution and D) DEM (Thematic at 50m interval)

The following plot of distance (X-axis) vs elevation (Y-axis) also exhibits a good agreement of the ASTER DEM with topographic map derived DEM with minor variation in positional accuracy.

The following points have been important, while creating good DEM:

  • Collecting most of the GCPs in low altitude areas and very less (1 or 2 only) on maximum elevation. The reason being high altitude areas have lean and their image position does not reflect true ground position and hence X-Y references taken for such points results into large RMS error
  • Collection of GCPs on few hilltops (especially on the maximum elevation point in the area) is a must in order to define the full range of elevation in the output DEM. Otherwise, the high elevation areas yield large errors (>150m) due to lack of calibration
  • The GCPs must be well spread over the entire area
  • Large number of TPs help in getting better oriented epipolar images, thus the enhancing the chances of good raster match and hence good DEM with less failure areas
  • Running a second DEM process after modifying some GCPs and adding few more TPs (if required) after random evaluation first round of DEM generation helps in improving the accuracy and containing the failure areas

The comparison table reveals that all optical sensors derived DEMs are not very different in linear and circular errors. ASTER DEM, however, appears to be the best as the DEM from it could be derived at full resolution and less failure areas and the effective cost of DEM per sq. km. is the least.


*In the case of Radarsat LE 80 has been considered

Conclusion
Good quality DEMs can be created from different satellite image stereo pairs. The number and distribution of GCPs and TPs play a crucial role in the final accuracy of the DEM.

Layover and shadow effects in used Radar images were pronounced, rendering it a not so viable option for high relief areas. However, judicious selection of beam mode combination and by using more than one pair would give better results.

Study reveals that, of the three different pairs of optical sensor data processed, the ASTER images yield the best results. The elevation range and relief covered by ASTER image in this sample study was also significantly large (2700m approx.). The better results appear to be due to the negligibly small temporal difference between the stereo pair (only four seconds), whereas pairs acquired through other satellites in optical mode have comparatively large temporal variation. Further, the unique fixed b/h ratio yields more consistent and better results over various terrains. The net cost of DEM derived from ASTER images also is very less as compared to IRS and SPOT images, which cost nearly three times more than ASTER.

Since the output DEM reflects a pixel level true picture of the terrain, the DEMs thus created can be used for various application areas of GIS like Telecom, Watershed delineation, 3-D analysis etc.

References

  • Thierry Toutin, “Generating DEM from Stereo-Images with a Photogrammetric Approach: Examples with VIR and SAR data,” EARSeL Advances in Remote Sensing, Vol. 4, No. 2, p. 110-117, 1995.
  • Marty Marra, Kelly E. Maurice, Dennis C. Ghiglia, Heinrich G. Frick,” Automated DEM Extraction Using RADARSAT ScanSAR Stereo Data”, From Web
  • Enrico C. Pedroso, Alvaro P. Crósta and Carlos R. de Souza F, “Comparative Analysis of RADARSAT and JERS-1/SAR DATA for Geological Mapping and Gold Exploration in The Tapajós Region, Brazilian Amazon”, From Web.
  • Thierry Toutin and Ph. Cheng, “DEM Generation with ASTER Stereo Data”, Earth Observation Magazine, Vol. 10, No. 6, pp. 10-13, June 2001.

Selected reading
Thierry Toutin, “3D Topographic Mapping with ASTER Stereo Data in Rugged Topography”, Personal communication to the authors, 2001.

PCI Geomatics, “OrthoEngine SE 3D, v8.2” in Reference Manual, Version 8.2 (October19, 2001).