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FUSION OF OPTICAL AND INSAR DEMS: IMPROVING THE QUALITY OF FREE DATA. Manoj Karkee, Michiro Kusanagi, Frederic Borne and Marc Souris Abstract: (Key words: DEMs, Stereoscopy, Interferometric SAR, Data Fusion, Spatial Frequency) Spaceborne remote sensing is being used widely for decades to generate Digital Elevation Models (DEMs). Optical Stereoscopy and Synthetic Aperture Radar Interferometry (InSAR) are the two major satellite techniques of the topographic measurement. Both of these methods of generating DEMs have their own limitations. Image matching in stereoscopy suffers from cloud cover, radiometric variation and low-texture whereas InSAR suffers from layovers, shadows and temporal decorrelations. These inherent problems cause voids and interpolated data in final product. This paper presents an approach of DEM fusion to improve the overall quality and applicability of DEMs with special measures to the two of the public products viz. Shuttle Radar Topographic Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and Reflection (ASTER) DEM. First, the relative ASTER DEM is coregistered to SRTM co-ordinates and converted to near absolute DEM by shifting histogram to the average elevation of SRTM DEM. Voids in the DEMs are filled by taking slopes and aspects from another DEM and using elevation of surrounding pixels. Finally, higher frequency components of Optical DEM and lower frequency components of InSAR DEMs are filtered out, as they are more erroneous respectively. Filtered DEMs are then added to generate fused DEM. This approach was tested in a 60 KM2 site located in Central region of Nepal having fairly complex topography. The accuracy of resulting DEMs was assessed in terms of error map and error statistics. Fused DEM shows up to 45% improvement in accuracy measures. The approach is promising to improve the accuracy and completeness while maintaining the resolution to the best of participating DEMs. This approach increases the applicability of free and commercial DEMs produced from Optical and SAR remote sensing. Authors:
Manoj KARKEE* Master’s Degree Student Email:
[email protected], Tel: +66-2-524-7861 Specialization: Interferrometric SAR, Data Fusion, Image Processing, Neural Networks in Remote Sensing. Michiro KUSANAGI, PhD* Professor Email:
[email protected], Tel: +66-2-524-6040 Specialization: Aerospace System Engineering, Space System Engineering Frederic BORNE, PhD* Assistant Professor Email:
[email protected], Tel: +66-2-524-5592 Specialization: Image Analysis, Image Processing, Texture Analysis, 3D Landscape Modeling, Linkage between plant growth modeling and RS/GIS applications. Marc SOURIS, PhD* Visiting Research Scientist Email:
[email protected], Tel: +66-2-524-6125 Specialization: Remote Sensing and GIS development *Remote Sensing and GIS Field of Studies Map India 2005
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Asian Institute of Technology P.O. Box 4, Khlong Luang Pathumthani, 12120, THAILAND Fax: +66-2-524-5597 Introduction: Spaceborne remote sensing is being used widely for decades to generate DEMs. Though Interferometric SAR (InSAR) is relatively new technology (started mainly with ERS 1, launched in 1991), today, both Stereo-Optical and InSAR techniques exist in parallel and are applied independent of each other, although several synergies to overcome the limitations of each technique can be identified. These techniques are different from each other and they have been described in several publications [Zebker et al. 1986, LaPrade 1980, Toutin 1995]. Image parallax or the coordinate difference between conjugate points in two partially overlapping images is used in optical technique to extract height information, whereas InSAR technique computes the height from the phase difference of the point backscatter received by either the same antenna in two passes or two different antennas of the same mission separated by some distance. Due to the inherent difficulties in acquiring satellite data both with optical and RADAR technology, they are not complete in themselves. Image matching in stereoscopy suffers from cloud cover, radiometric variation and lowtexture whereas InSAR suffers from layovers, shadows and temporal decorrelations. InSAR also suffers from the temporal decorrelation [Zebker et al. 1992] and changes in atmospheric conditions between two acquisitions [Zebker et al. 1997]. Due to these inherent problems DEMs generated from both of these techniques contain holes, interpolated data and estimated values. SRTM and ASTER from Earth Observing System (EOS) of NASA are providing two different types of near global DEMs, former an optical product and the later a SAR product. Both are principally free to public use. Problems comes from the low spatial resolution of SRTM product for outside the territory of USA, which is 3 arcseconds (~90m), and from the difficulty in ordering absolute ASTER DEM from EOS. EOS provides only relative DEMs if the user fails to submit three dimensional Ground Control Points (GCPs) and we have to purchase level 1A data of the required scene and some field survey is involved to generate such GCPs. These activities involve the cost, which limits the applicability of free DEMs. Synergy of the Fusion: Synergy of DEM fusion comes from the techniques by which optical and InSAR DEMs are produced and from the reasons of failure of the individual techniques. We can predict complementarities in error behavior of Stereoscopy and InSAR. Stereo-optical DEM generation fails, as stated above, in presence of clouds or low texture, while InSAR works fine in such cases. On the other hand, InSAR fails under conditions like terrain steepness or surface roughness, both handled well in stereoscopy. Problems of the InSAR technique like layover, temporal decorrelation or atmospheric effects may affect the phase of large areas in which the interferometric height determination fails leading to a regional error in DEMs. This phenomenon causes smoothing of DEM and thus introduces low frequency component in frequency domain. In contrast to the InSAR case, the stereo optical elevation model appears much more rugged and is much less interpretable, as the detailed course of the terrain is hidden behind induced spikes originated from the mismatching of conjugate points in the image pair. Errors occur therefore locally, typically as jumps between two adjacent pixels, respectively height values [Honikel 1998], which leads to high frequency components in the spatial frequency domain. [Honikel 1998] used the synergy between optical DEM and RADAR DEM in spatial frequency domain. He filtered out error prone components of SPOT DEM and ERS-1 DEM and added them in frequency domain resulting to an improved DEM in terms of RMSE and error distribution. This paper introduces an approach of data fusion to improve the overall quality of the freely available DEMs. The methodology addresses problems resulted from technological limitations of the optical stereoscopy and InSAR and it also exploits the good aspects of one product to minimize the shortcomings of another product.
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Methodology: Fig. 1 depicts the flow of data through different processes in Fusion Schema. Reference DEM
Optical DEM (ASTER)
InSAR DEM (SRTM)
Co-registration
Co-registered ASTER DEM
Legends:
Convert to Frequency Domain
Inputs Processes Intermediate Results
ASTER Spectrum
Remove Higher Frequencies
SRTM Spectrum
Remove Lower Frequencies
Outputs ADD
Convert to Spatial Domain
Fused DEM
Accuracy Assessment Fig. 1: Flowchart of the DEM Fusion Schema. Flp(p,q) = F(p,q)Hlp(p,q) Where, p,q: Pixel location in frequency domain Flp(p,q): Resulted Course Terrain F(p,q): Stereo Optical DEM Hlp(p,q): Ideal low pass filter given by
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1. Co-registration and Void Filling: Relative ASTER DEM is co-registered to SRTM DEM. It is a GCP based co-registration. Selecting GCPs in DEM is a truly difficult job. To simplify, valley lines and ridge lines are derived from the DEMs and symmetrical crossings of such geomorphic lines are carefully noted as GCPs. Registered ASTER DEM is converted to near absolute DEM by shifting histogram to the average elevation coming from SRTM DEM. To fill the voids of one DEM, slope and aspect of the void pixels are derived from elevations of another DEM, provided the area is not void in second DEM. These slope and aspect values, along with the absolute elevation of neighboring pixels are then used to determine the elevation of void pixels. 2. Conversion to Spatial Frequency Domain: It is simply achieved by taking Fast Fourier Transform (FFT) of the DEMs. 3. Removing High Frequency Components of ASTER DEM: An ideal low pass filter can be implemented for this purpose. The filtering operation is represented by following equation [Honikel 1998].
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1 H lp ( p, q ) = 0
for ( p 2 + q 2 ) < ϖ 0 Otherwise
Determining good value of ϖ 0 may seek some experiments specific to a particular topography. 4. Removing Low Frequency Components of SRTM DEM: Similarly to step 3. 5. Addition and Conversion to Spatial Domain: Addition is simply a numerical sum and inverse FFT serves the purpose of converting fused DEM into spatial domain. Test Site and Data: Testing of the proposed methodology of DEM fusion has been conducted to a test site located in central region of Nepal (85°32’ E, 27°36’N, 1530m asl) some 25 KM east of Kathmandu. Test site covers about 60 KM2 terrain consisting of varying topography including both plain valleys and very rugged hilly region (slopes up to 60 degree); and the elevation range of 655m (Maximum-1840m and Minimum-1185 m asl.). Two entirely free DEMs have been used in this research to implement the fusion algorithm. We had downloaded 3 arcsecond (~90m) SRTM InSAR DEM and ordered 30m ASTER relative DEM of the test site. SRTM DEM was oversampled to 30m to make the dataset of the same resolution. Seasonal cultivation is the main landuse type in low land area whereas forest (mainly pine trees) is the main cover of upland region. Reference data for the study had been bought from the Survey Department of Nepal. This is a 1:25000 contour map developed by Survey Department of His Majesty’s Government of Nepal in co-operation with the Finish International Development Agency (FNNIDA). This contour map was interpolated with ArcView 3.2 into the reference DEM of 30 m pixel size. UTM with Indian datum is the reference projection system where the test site falls over zone 45N. Results: SRTM DEM of the test site (Fig 2.a) shows stripped look as it had been oversampled to 30m from ~90m pixel. The void in the central hill slope may be due to the geometry related constraints like shadows and foreshortening. Fig 2.b is the ASTER relative DEM having a void in the northwestern hilltop, which may be due to the cloud cover in the Level 1A images. Error maps were computed by subtracting individual DEM from reference DEM. For instance, errors shown in Fig 2(d) are in the signed magnitude form. So, darker regions represent higher negative errors whereas lighter regions represent higher positive errors. Gray region is the lowest absolute error area. Fig. 3 depicts the histograms of the error maps and key attributes of the histograms are shown in Table 1. SRTM DEM has higher average elevation than the reference DEM having mean error of –8.7m. The DEM has negligible number of pixels having absolute error greater than 100m and 2.4% pixels having the absolute error within 50m to 100m.
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a. SRTM DEM
b. ASTER DEM
c. Fused DEM
d. Error Map
Fig. 2: SRTM DEM with a void caused by RADAR Geometry (a), ASTER Relative DEM shifted to SRTM mean elevation having a void caused by cloud cover (b), Fused DEM benefited from the complementarily of two DEM which avoids voids and improves the accuracy (c) and final error map generated by subtracting fused DEM from reference DEM (d). ASTER DEM has been co-registered and shifted to the center elevation of SRTM and thus the error mean is same as SRTM DEM. But, it is worse in the measures like standard deviation and range of the error. The standard deviation of the error is 28.3m where as the same for SRTM DEM is 18.3m. Fused DEM shows the improved accuracy, completeness and reliability. It shows significant improvement in the accuracy as compared to the ASTER DEM whereas maintaining the resolution. Though the mean error of the fused DEM is same as input DEMs, we observed 44% improvement in the standard deviation of error with respect to ASTER DEM. It is 12% better than the errors with respect to SRTM DEM. Peak errors (above 50m absolute) has been reduced to 2.3% from 7% in ASTER and 2.4% in SRTM. Error range of the resulting DEM reduced to 201m (Min: -119m, Max: 82m) from 308 (Min: -169m, Max: 139m) of ASTER DEM and from 239m (Min: -121m, Max: 118m) of SRTM DEM. Though the SRTM DEM was of ~90m resolution, the fusion results to a 30m DEM same as the resolution of ASTER relative DEM.
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Histograms of Error Map Frequency->
Error Maps SRTM ASTER Fused
Min (m) -121 -169 -119
Max (m) 118 139 82
Mean (m) -8.7 -8.7 -8.7
SD (m) 18.3 28.3 16.4
SRTM ASTER
Error Abs Error 100m>Abs Maps >100m Error>50m SRTM 0.0% 2.4% ASTER 0.6% 6.4% Fused 0.0% 2.3%
After Fusion
-200
-150
-100
-50
0
50
100
150
200
Table 1: Key measures of the Histograms shown in fig 3.
Errors (m)->
Fig 3: Histograms of Error Maps. Mean of all the histogram are same. Improvement of the error statistics after fusion can be seen in the reduced standard deviation and reduced range of errors. The sharper shape of the error histogram after fusion depicts the fact. Conclusion: Proposed methodology of data fusion takes benefit from better aspects of DEMs participating to the fusion process and results to a significant improvement in accuracy as compared to ASTER relative DEM (up to 44% improvement in standard deviation and range of the error map) and improved resolution with respect to the SRTM DEM. It also fills the voids and increases the reliability of the product. In effect, free and commercial satellite DEMs gets more applicable. Few future works are expected to perform well for better results. GCP based co-registration applied in this research has the typical accuracy of few pixels. In future, more accurate co-registration techniques like the one based on image co-relation will be implemented which will have sub-pixel level accuracy. Validity of the results is subjected to the more number of tests. Proposed methodology will be checked for new test sites having different types of topography. Improvement in void filling algorithm is also essential for more fruitful results. References: 1. Crosetto, M. and B. Crippa [1998], ‘Optical and RADAR data fusion for DEM generation’. IAPRS, Vol. 32, Part 4. 2. Ghiglia, D.C and M. D. Pritt [1998]. ‘Two-dimensional phase unwrapping: theory, algorithms and software’. John Wiley & Sons, New York, USA, 493 p. 3. Graham, L.C. [1974]. ‘Synthetic Interferometer Radar for Topographic Mapping’. Proc. IEEE, Vol.62, 763768. 4. Harold, R. and L. Roy [1999]. ‘Welch algorithm theoretical basis document for aster digital elevation models (standard product ast14)’. Version 3.0 revised 5 february 1999. 5. Honikel, M. [1998]. ‘Fusion of Optical and Radar Digital Elevation Models in the Spatial Frequency Domain’. Retrieval of Bio- and Geo-Physical Parameters from SAR Data for Land Applications Workshop ESTEC, 21-23 October 1998. 6. Honikel, M [1999]. Strategies and methods for the fusion of digital elevation models from optical and SAR data’. International Archives of Photogrammetry and Remote Sensing, Vol 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999. 7. Kamp, U., T. Bolch and J. Olsenholler [2003]. ‘DEM generation from ASTER satellite data for geomorphometric analysis of Cerro Sillajhuya, Chile/Bolivia’. ASPRS 2003 Annual Proceedings, May 2003, Anchorage, Alaska, U.S.A., 9 pp. Map India 2005
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8. Koch, A., C. Heipke and P. Lohmann [2002]. ‘Analysis of SRTM DTM: Methodology and Practical Results’. Geospatial Theory, Processing and Applications ISPRS Commission IV, Symposium 2002 Ottawa, Canada, July 9-12, 2002. 9. Rabus, B, M. Eineder, A. Rith and R. Bamler [2003]. ‘The Shuttle Radar Topography Mission- A New Class of Digital Elevation models acquired by space borne radar’. ISPRS Journal of Photogrammetry and Remote Sensing, Vol 57. 10. Rao, Y.S., K.S. Rao, G. Venkataraman, M. Khare and C. D. Reddy [2003]. ‘Comparison and Fusion of DEMs Derived from InSAR and Optical Stereo Techniques’. Third ESA International Workshop on ERS SAR Interferometry, Fringe 2003. 11. Topographic Science Working Group [1988]. Topographic Science Working Group report to the Land Processes Branch, Earth Science and Applications Division, NASA Headquarters: Lunar and Planetary Institute, Houston, Texas, 64 p. 12. Toutin, T., L. Gray [2002]. ‘State-of-the-art of elevation extraction from satellite SAR data’. ISPRS Commission III Symposium, September 9 - 13, 2002 Graz, Austria. 13. Zebker, H. and R. Goldstein [1986]. ‘Topograhic Mapping from Interferometric Synthetic Apeture RADAR Observations’. J. Geo. Phys. Research, Vol. 91, B5, pp. 4993-4999. 14. Zebker, H. and J. Villasenor [1992]. ‘Decorrelation in Interferometric RADAR Echoes’. IEEE Trans. Geo.Sci. Remote Sensing, 30(5), pp.950-959. 15. Zebker, H.A., P.A. Rosen and S. Hensley [1997]. ‘Atmospheric Effectgs in Interferometric Synthetic Aperture RADAR Surface Deformation and Topographic Maps’. J. Geo. Phys. Res., vol. 102, B4, pp.75477563.
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