RADARGRAMMETRY AND INTERFEROMETRY SAR FOR DEM GENERATION

Transcription

1 RADARGRAMMETRY AND INTERFEROMETRY SAR FOR DEM GENERATION Jung Hum Yu 1, Xiaojing Li, Linlin Ge, and Hsing-Chung Chang School of Surveying and Spatial Information Systems University of New South Wales, Australia , Jung.yu@student.unsw.edu.au Abstract Digital elevation models (DEMs) can be generate by interferometric SAR (InSAR) and radargrammetry techniques from different positions of Synthetic Aperture Radar (SAR) images. Radar imaging systems record both the phase (or time) and intensity information of the backscattered signals. InSAR utilises the phase information of the images to extract useful geodetic information, such as the height of terrain, ground deformation. However, InSAR technique is constrained by the temporal and spatial separations between the images used, as well as the various atmospheric conditions at the time of acquisitions. In comparison, radargrammetry technique utilises the intensity information in a stereo-pair of radar images. That is similar to stereogrammetry or photogrammetry which is a classic method for relief reconstruction using airborne/spaceborne optical images. In this paper, the advantages and disadvantages of both InSAR and radargrammetry for DEM generation are demonstrated using the ALOS/PALSAR and Envisat/ASAR data. Introduction There are two major techniques for DEM generation using SAR data. One is based on interferometric SAR (InSAR) and the other is based on radargrammetry. In general, higher accuracy DEM can be generated through using InSAR technique; therefore, InSAR is used when application. InSAR method is relatively cost efficient and effective, also wide-coverage on DEM generation. This technique involves interferometric phase comparison from two SAR images acquired at different positions with a separation of perpendicular baselines. It can generate DEMs at metre-level accuracy. InSAR extracts information of the terrain from the phase difference interferogram of two SAR data images (Ferretti et al. 2001). However, InSAR DEM generation is subject to decorrelation, atmospheric disturbance and the conditions on incidence angle and Doppler similarity are stringent (Massonnet and Souyris 2008). InSAR need the expectations of a certain range baseline because the interferometry is sensitive to the direction of sensor movement and some other factors. To overcome those limitations, the radargrammetry technique is then an important alternative for DEM generation (Chen and Dowman 2001). The main difference of two techniques is that radargrammetry calculates the image range offset using the position matching of the same ground targets in two images

2 while InSAR calculate the phase difference of two images (Kyaruzi 2005; Sansosti 2004). Radargrammetry is based on stereogrammetry which is a classic method for relief reconstruction using optical remote sensing images. This technique can be applied to radar images for generating good quality DEMs (Paillou and Gelautz 1999). One of the advantages of radargrammetry is less affected by atmospheric influence compare with interferometry. Basically, atmospheric effect on the SAR imagery is same in the radargrammetry or in the InSAR. However, radargrammetry uses the magnitude (intensity) value while InSAR uses the phase difference in SAR imagery. Considerably, magnitude is less affected than phase by atmospheric heterogeneous. The atmospheric disturbance is undesirable for the InSAR processing but not much of a concern for the radargrammetry processing (Massonnet and Souyris 2008). But the radargrammetry uses stereoscopic pairs acquired from different incidence angles. Also, the radargrammetry is different with photogrammetry mainly in three aspects (Schanda 1985): (1) the interaction effects with the surface at radar wavelengths are different from those at optical wavelengths, (2) radar measures distance between sensor to target, therefore the parallax appears reversed when compared with optical image, (3) the long wavelengths cause poor angular resolution at a given size of optics therefore a useful stereo base cannot be established simultaneously from one simple platform. Furthermore, stereoscopic pairs for radargrammetry should be considered that the geometry and parallax produced from a particular system configuration and the awareness of the image pairs by the interpreter. Obviously, the radargrammetry technique for terrain elevation extraction requires the conditions of have approximately 10~20 degrees incidence angle difference and overlapping of two images between input image pairs (Mercer 1995; kaupp et al. 1983). In this paper, the authors discuss and demonstrate the advantages and disadvantages of both InSAR and radargrammetry using the real satellite data. Radargrammetric DEMs generated using the stereo-image pairs with various look-angles, baselines, ascending and descending orbits are examined closely. Methodology DEM generation using Radargrammetry Radargrammetry requires image acquisitions with varying incidence angles. The quality of radargrammetry DEM depends on the base to height ratio or intersection angle of the radargrammetry pair demonstrated in Figure 1. To acquire good geometry for radargrammetry pairs, the intersection angle between the two SAR images should have enough angles for the observed parallax which is used to determine the terrain elevation. However, in order to have good stereo-viewing, the nearly identical images (small intersection angle) are necessary in processing. Approximately degree intersection angles between the two images and shallow look angle (i.e. angle between vertical and the bean direction>20 ) are usually considered on optimal configurations for medium to high relief areas (d Ozouville et al. 2008). Thus, a compromise has

3 to be reached between a better stereo-viewing and more accurate elevation determination (Toutin and Gray 2000). z S 1 S 2 H B x θ 1 θ 2 B z y R 1 R 2 x 2 P P 2 x 1 h dp P 1 x Figure 1. The different observation positions and geometry for radargrammetry (Maitre, 2008) Where in Figure 1, S 1, S 2 are the satellites, B x, B z is the horizontal and vertical baseline, R 1,R 2 are the distances between the sensors and ground target P. The target P is seen as P 1 and P 2 in both SAR images from S 1 and S 2. Then, dp is called disparity distance of P 1 and P 2. If the ground elevation is zero (h=0), disparity will be zero and it increments for increasing heights h. It is expressed by: dp= x + ( H h) H ( x Bx ) + ( H+ Bz h) ( H+ Bz ) Bx (1) More importantly, the elevation calculation has the reverse relationship which gives elevation h for the disparity point dp HBx + 2Hdp 4H Bx + dpλ h = (2) dp + Bx there Λ = 8B x( H x + xbx ) + dp(4bx + dp + 4dpBx ) + 4dp( H x + xbx ) (3) These equations are simplified further as the images are acquired from satellites sensors (sensor elevation, H, is significant compared to target altitudes, h). Therefore, the equation can be a simple expression in accordance with look-angles of θ 1 andθ 2 : dp h = (4) cotanθ 2 cotanθ1 Figure 2 shows the processing steps of radargrammetry for DEM generation. The radargrammetry DEM processing steps can be described in terms: (1) acquiring stereoscopic images; (2) subset in the areas of interest; (3) despeckle to remove the noise; (4) co-registration of two subset images; (5) matching between co-registered images; (6) height calculation; (7) geocoding and DEM generation.

4 Reference image Subset image Despeckle Match image Subset image Despeckle Co-registration Matching Height calculation DEM generation Figure 2. The flow-chart of radargrammetry processing DEM generation using InSAR The phase differences of radar wavelengths recorded by SAR images are able to provide precise measurement, to the sub-wavelength level, of the range or distance between the location of the observing antennas and the points of reference ( pixel centres ). With two observing antennas, or the same antennas at different satellites overpass times, differential phases can provide information on terrain elevation or on terrain displacement (Toutin & Gray 2000). In the InSAR processing, it is crucial that the imaging geometry of the first path is repeated as closely as possible in the second path. In other words, the location difference (or perpendicular baseline ) must be kept as short as possible - typically less than a kilometre. The reason for this condition is due to the geometric relationships utilised in the interferometric processing of images. However, in the DEM generation, the perpendicular baseline prefers long baseline images for increase the terrain sensitivity. SAR images have a spatial resolution defined by the pulse length, altitude of the satellite and look-angle. The slant range resolution is half the pulse length. The ground range resolution is defined as the slant range resolution divided by the cosine of the look-angle. The set of phase differences produced for all pixels of the two SAR images is used to generate an interferogram. The SAR interferometry method allows to generation of DEM, as well as the detection and measurement of ground deformation. In producing an InSAR DEM, the difference in the phase of at least two SAR images of the same area identifies the interferometric phase contribution due to the terrain: 4π φ = ( R ) (5) λ 1 R 2 where λ is the radar wavelength (Crosetto 2002; Zhou et al. 2005).

5 Interferometry A 2 A 1 B s H R 1 R 2 Δθ I Azimuth P h Figure 3. Geometry of Interferometric SAR (InSAR) In Figure 3, H is the altitude of the imaging satellite, R 1, R 2 are the distance between the target P on the ground and satellite antennas A 1 and A 2 respectively. B s is the distance between the two antennas A 1 and A 2 at the satellites imaging locations. The elevation information is obtained from the interferogram by unwrapping the interferometric phases (e.g. Ferretti et al. 1999). The interferogram has three folds of information: 1) topographic information, 2) surface displacement that has taken place between the two SAR image acquisitions, and 3) atmospheric delay and noise. The atmospheric effect is primarily due to the water vapour content in the atmosphere between the satellite radar sensor and the ground target. The relation in phases therefore is: φ InSAR= φflat+ φtopo+ φdefo+ φatm+ φnoi (6) where φ is the interferometric phase, InSAR φ flat is the so-call flat earth phase, φ is the topographic phase, topo φ defo is the deformation phase, φ atm is the atmospheric delay phase and φ is the noise. The atmospheric delay noi component φatm can be identified using the fact that its phase difference patterns are independent over several interferograms, or, alternatively, it can be modelled by using a GPS ground network to independently determine the atmospheric delay, or by a technique known as interferogram stacking. In equation (6), the flat earth phase φ flat and noise φnoi can be removed by using the orbit information correction and applying an interferogram filtering method. When the imaging interval is sufficiently short it may be assumed that there is no deformation phase φ defo. InSAR is the process to extract the topographic phases while to eliminate other undesired phase components. If the atmospheric delay phase φatm can be ignored (or determined from other sources), then, the equation (6) can be reduced to: φ = (7) InSAR φ topo Range

6 The DEM is obtained by unwrapping the phaseφ, then converting the phase to a height for that pixel, and then geo-coding each pixel in turn. The attention also is, the pixel coordinates and parameters of the InSAR DEMs are dependent on the parameters of the master image (Ferretti et al. 2001). Master SLC Slave SLC Co-registration Interferogram Coherence Denoise Interferogram Phase Unwrapping Phase to Height Geocoded Products Figure 4. The flow chart of InSAR DEM generation The unwrapped phases have to be converted in terrain heights. Phase to height conversion is the procedure which relates the unwrapped phase to topographic height. Experimental Result The test site has been set at the Appin area in the state of New South Wales, Australia. This research used the Advanced Land Observing Satellite (ALOS/PALSAR) SAR image (L-band) and European Remote Sensing Satellite (ERS-1) SAR image (C-band) to InSAR generated DEMs. Table 1 presents the information of SAR data. In order to improve the coherence of image pairs, short temporal baseline is selected for InSAR DEM generation. The Shuttle Radar Topography Mission (SRTM) DEM is used as the external DEM. Table 1. The information of InSAR processing images Sensor Master date Slave date Bperp (m) Btemp (days) ALOS 27/12/06 11/02/ ALOS 14/02/08 31/03/ ERS 29/10/95 30/10/ ERS 03/12/95 04/12/

7 In radargrammetry DEM processing, four pairs of ALOS/PALSAR and four pairs of ENVISAT/ASAR images were used. The characters of radargrammetry allow larger coverage processing than interferometry method. Images were acquired from different path orbits which have different incidence angle. Table 2 is the image information used in radargrammetry. The same-side stereo method was used in radargrammetric DEM generation. Table 2. The information of radargrammetry images Sensor Reference image Match image Incidence angle_reference Incidence angle_match ALOS 31/03/08 (370) 05/04/08 (373) ALOS 07/04/08 (365) 05/04/08 (373) ALOS 23/05/08 (365) 21/05/08 (373) ALOS 01/07/08 (370) 06/07/08 (373) ENVISAT 18/12/09 (152) 26/09/09 (467) ENVISAT 02/04/10 (152) 31/10/09 (467) ENVISAT 15/03/10 (402) 12/03/10 (359) ENVISAT 08/02/10 (402) 05/02/10 (359) Figure 5. The radargrammetry intensity images of 31/03/ /04/2008 ALOS/PALSAR pair (31/03/2008: reference image-left, 05/04/2008: matching imageright). Table 3. The information of intensity average processing images Track (angle) Date 338 (18.9 ) 02/10/ /11/ /12/ (28.7 ) 05/10/ /11/ /12/ (33.6 ) 24/10/ /11/ /09/2008 Table 3 lists the data information which was used to re-generated intensity SAR images using averaging method. Figure 5 shows the intensity images for radargrammetry technique. Reference image has a incidence angle of 38.7

8 and matching image has incidence angle of 47.3 from satellite sensor. The terrain shapes are appeared different appearances such as distance and width difference between rivers and size of glass area. These phenomenons generate the stereoscopy of radargrammetry. The reference and match images were registered for pixel offset calculation. Automatic matching method calculates the correlation between two images for generate a disparity map. Matching can be performed using initial gray-level images, edge images, or some other image features such as linked-edge elements or regions. The main problems encountered when matching radar images for radargrammetry are speckle noise and that the difference of two stereo partners from one another, as the backscattered radar signal mainly depends on the local incidence angle (Paillou and Gelautz 1999; Tountin and Gray 2000). Figure 6. The correlation image between reference image and match image after matching process Figure 6 shows the correlations between reference and match. The correlation number gives a measure of how strong a match exists between the reference and match points. The correlation scale ranges from 0 to 1, where the closer the result is to 1, the better the match. The higher correlations are appeared in city area like the Appin and Wollongong and surround the rivers. Figure 7 shows the InSAR generated DEM results using ALOS/PALSAR pairs in test area. Same parts of DEM have elevation errors due to the ground deformation between different acquisitions and due to the low coherence of two images. Figure 8 shows the InSAR generated DEMs result using ERS pairs in test area. ERS images provide the larger coverage and shorter data processing time than PALSAR images. Figure 9 and 10 show the radargrammetry DEM results using Envisat /ASAR pairs in test area. In the DEM results, the elevation errors were appeared in near shoreline and ocean area. Figure 11 illustrates the radargrammetry DEM results using ALOS/PALSAR pairs in test area. Figure 12 presents the radargrammetry DEMs using Envisat intensity average image which was processed by three different acquisition time.

9 Figure 7. The InSAR DEM generated from ALOS/PALSAR (left: 14/02/2008~31/03/2008, right: 27/12/2009~11/02/2007) Figure 8. The InSAR DEM generated from ERS (left: 03/12/1995~04/12/1995, right: 29/10/1995~30/10/1995) Figure 9. Radargrammetry DEM generated from Envisat /ASAR (left: 08/02/2010~05/02/2010, right: 15/03/2010~12/03/2010)

10 Figure 10. Radargrammetry DEM generated from Envisat /ASAR (left: 02/04/2010~31/10/2009, right: 18/12/2009~26/09/2009) Figure 11. Radargrammetry DEM generated from ALOS/PALSAR (left: 31/03/2008~05/04/2008, right: 01/07/2008~06/07/2008) Figure 12. Re-generated radargrammetry DEM from Envisa intensity average images (left: track 338 and 152, right: track 338 and 381) Concluding remarks,two types of wavelength SAR imageries and two DEM generation methods were used for DEM generation in this paper. The DEM generation using radar imageries is operated by InSAR technique for high accuracy products generation and the radargrammetry is the superseded technique of InSAR for DEM generation method. Radargrammetry

11 is comparable to photogrammetry in that a stereo parallax found in a stereo image pair corresponds to terrain elevation. The advantage of the radargrammetry technique for DEM generation is that it can provide larger coverage per processing time and software capability than InSAR processing. However, the main problem of radargrammetry is that the DEM products have low level quality of DEM due to the spatial resolution of SAR imageries and the terrain slope. The disparity and convergence of objects are the two cues when viewing stereo imagery. Disparity is predominating when viewing radar image, but the shade and shadow cues also have a strong and cumulative effect on accuracy. Especially, the range direction errors in radargrammetry are higher than azimuth direction errors. Furthermore, smaller intersection angle provides the more accurate DEM products. References Chen, P.H., and Dowman, I.J., 2001, A weighted least squares solution for space intersection of spaceborne stereo SAR data. IEEE transactions on geoscience and remote sensing, 39(2): Crosetto, M., 2002, Calibration and validation of SAR interferometry for DEM generation. ISPRS Journal of Photogrammetry & Remote Sensing, 57(3): d Ozouville, N., Deffontaines, B., Benveniste, J., Wegmuller, U., Violette, S., and Marsily, G., 2008, DEM generation using ASAR (ENVISAT) for addressing the lack of freshwater ecosystems management, Santa Cruz Island, Galapagos. Remote Sensing of Environment, 112(11): Ferretti, A., Prati, C., and Rocca, F., 1999, Multibaseline InSAR DEM reconstruction: The wavelet approach. IEEE Transactions on Geoscience & Remote Sensing, 37(2): Ferretti, A., Prati, C., and Rocca, F., 2001, Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience & Remote Sensing, 39(1): Kaupp, V. H., Bridges, L. C., Pisaruck, M. A., MacDonald, H. C., and Waite, W. P., 1983, Simulation of spaceborne stereo radar imagery: Experimental result. IEEE transaction of geoscience and remote sensing, GE-21(3): Kyaruzi, J. K, 2005, Quality assessment of DEM from radargrammetry data. International institute for geo-information science and Earth observation enschede, The Netherlands, master of science. Maitre, H., 2008, Processing of synthetic aperture radar images. ISTE Ltd and John Wiley & Sons Inc.

12 Massonnet, D., and Souyris, J. C., 2008, Image with synthetic aperture radar. EPFL Press, Mercer, J. B., 1995, SAR technologies for topographic mapping. Photogrammetric Week 95, Paillou, P., and Gelautz, M., 1999, Relief reconstruction from SAR stereo pairs: the Optimal Gradient matching method. IEEE transactions on Geoscience and Remote Sensing. 37(4): Sansosti, E., 2004, A simple and exact solution for the interferometric and stereo SAR geolocation problem. IEEE transactions on Geoscience and Remote Sensing, 42(8): Schanda, E., 1985, A radargrammetry experiment in a mountain region. International Journal of Remote Sensing, 6(7): Toutin, T., and Gray, L., 2000, State-of-the-art of elevation extraction from satellite SAR data. ISPRS Journal of Photogrammetry & Remote Sensing, 55: Zhou, C., Ge, L., E, D., and Chang, H.C., 2005, A case study of using external DEM in InSAR DEM generation. Geo-Spatial Information Science, 8(1):