Measuring Ground Deformation using Optical Imagery

Transcription

1 Measuring Ground Deformation using Optical Imagery Sébastien Leprince California Institute of Technology, USA October 29, 2009 Keck Institute for Space Studies Workshop

2 Measuring Horizontal Ground Displacement, Methodology Flow P r e - e a r t h q u a k e i m a g e Inputs: Raw images Orbits, platform attitudes, camera model Digital Elevation Model Orthorectification: Images must superimpose accurately Correlation: Sub-pixel Correlation P o s t - e a r t h q u a k e i m a g e Displacement in rows and columns provide the E/W and N/S components of the ground deformation Outputs: N/S offset field E/W offset field SNR The Signal to Noise Ratio assesses the measure quality.

3 Orthorectification Model Pushbroom acquisition geometry satellite velocity look direction of pixel (c, r) absolute pointing error from ancillary data M PSat = O CCD acquiring column c CCD array O, optical center in space M, ground point seen by pixel p u 1 pixel pointing model R(p) 3D rotation matrix, roll, pitch, yaw at p T(p) Terrestrial coordinates conversion δ correction on the look directions to insure coregistration λ > 0 M(p) = O(p) + λ [ T(p)R(p) u 1 (p) + δ(p) ]

4 Image Correlation: local rigid translations Fourier Shift Theorem i 2 (x, y) = i 1 (x x, y y ) I 2 (ω x, ω y ) = I 1 (ω x, ω y )e j(ω x x +ω y y ) Normalized Cross-spectrum C i1 i 2 (ω x, ω y ) = I 1(ω x, ω y )I 2 (ω x, ω y ) I 1 (ω x, ω y )I 2 (ω x, ω y ) = ej(ω x x +ω y y ) Finding the relative displacement φ( x, y ) = π π W(ω x, ω y ) C i1 i 2 (ω x, ω y ) e j(ω x x +ω y y ) 2 ω x = π ω y = π W weighting matrix. ( x, y ) such that φ minimum. S. Leprince et al., IEEE TGRS, 2007

5 Processing Chain Select Image Registration Patches from raw image Orthorectify patches Resample image patches Correlate patches, find relative displacement with reference Deduce viewing correction δ for co-registration Orthorectify / resample image 1 Orthorectify / resample image 2 Correlation on sliding windows Horizontal deformation map S. Leprince et al., IEEE TGRS, 2007

6 1999 Mw 7.1 Hector Mine Earthquake, CA

7 1999 Mw 7.1 Hector Mine Earthquake, CA

8 1999 Mw 7.1 Hector Mine Earthquake, CA

9 The 1999 Mw 7.1 Hector Mine Earthquake 34 10'N 34 20'N 34 30'N 34 40'N ³ A km Offset in meters -3.0 m A A' Location in km 'W 'W 'W 116 0'W A' 34 10'N 34 20'N 34 30'N 34 40'N ³ km -3.0 m 'W 'W 'W 116 0'W The Hector Mine horizontal coseismic field (NS and EW) once CCD distortions from SPOT4 and SPOT2 have been modeled during orthorectification. Accuracy better than 1/10 pixel.

10 The Mer de Glace Glacier, France (a) 6 54'0"E 6 56'0"E 6 58'0"E ³ A' A Mer de Glace Talèfre 45 52'0"N 45 54'0"N 45 56'0"N B B' Leschaux 2000 m 26 days horizontal displacement (m) (b) 6 GPS measurements 5 A - A' Raw correlation measurements m 3 longitudinal average 2 1 B - B' Displacement in meters over 26 days (c) 20 A A' 16 B B' Transversal distance (meters) Displacement over 26 days (m) Distance (km) along North direction S. Leprince, et al., EOS, 2008

11 The La Valette Landslide, France (a) (b) ³ 44 25'0"N 9 8 Absolute horizontal offset 7 (meters) A 2 A' B A' '45"N Distance along profile (Km) B' 44 24'30"N Horizontal displacement (meters) 44 24'15"N A Absolute horizontal offset (m) B B' Meters Distance along profile (m) 6 38'15"E 6 38'30"E 6 38'45"E 6 39'0"E 6 38'15"E 6 38'30"E 6 38'45"E 6 39'0"E SPOT5 2.5m resolution images, 09/19/ /22/2004 S. Leprince, et al., EOS, 2008

12 Geometrical Distortions: CCD misalignement 34 10'N 34 20'N 34 30'N 34 40'N ³ Secondary branch of the rupture km A Offset in meters A' -3.0 m +3.0 Interconnection inaccuracies of the linear CCD arrays of the sensor -3 A A' Location in km 34 10'N 34 20'N 34 30'N 34 40'N ³ km m +3.0 Topography artifacts generated from the CCD array distortions CCD artifact 'W 'W 'W 116 0'W 'W 'W 'W 116 0'W The Hector Mine horizontal coseismic field (NS and EW) showing linear artifacts due to CCD misalignment. The geometry of the CCD sensor has to be well modeled. S. Leprince et al., IEEE TGRS, 2008

13 Geometrical Distortions: CCD misalignement SPOT CCD distortions inter-array 0.1 Across-track (X) distortion in pixel discontinuity CCD number: Along-track (Y) distortion in pixel CCD Calibration model (1/100 pixel accurate) for SPOT 4-HRV1 S. Leprince et al., IEEE TGRS, 2008

14 ASTER attitude variations: The 2005 Mw 7.6 Kashmir Earthquake Northward offset (m) tions fr om Terr (meter a satelli s) te Alongtrack direction ³ 20 Km 73 20'E 'E Un-reco 34 10'N rded pi tch varia 34 20'N 34 30'N Fault rupture 73 30'E 73 40'E 73 50'E Northward component of the correlation from 15m ASTER images acquired on 11/14/2000 and 10/27/2005. Before, and after removing pitch artifacts (destripping). Deformation mostly perpendicular to the fault that could not be measured on the field Leprince et al., IGARSS 2007 / Avouac et al., EPSL, 2006

15 Topography error: modeling D = h(tan(θ 1 ) tan(θ 2 )) The measurement error D results from a trade-off between a well resolved topography and incidence angles difference. Scherler et al., RSE 2008 D lives in the plane (p1 Mp 2 ), called the epipolar plane. For pushbroom systems, this plane is generally in the across-track direction, hence EW components are usually affected the most by topo biases.

16 Aliasing effects in deformation maps: 2001 Bhuj earthquake using SPOT images Optical images often aliased (CCD do not properly sample instrument PSF) Aliasing effects produce white noise when acquisitions have different viewing geometry Aliasing strongly bias subpixel measurement when images have similar viewing geometry Image de-aliasing or single image super-resolution still an open problem and area of active research

17 Future challenge for large scale monitoring Thus far: Semi-automatic processing: manual selection of registration points. Sufficient for studies with a few dozen of images Only a handful of registration points is necessary per image The key to large scale processing: Automatic determination of a few robust registration points per image Techniques such as SIFT can be useful to achieve this goal Tricky problem when dealing with ground displacement, because registration points should be selected on stable ground

18 Future challenge for large scale monitoring Thus far: Semi-automatic processing: manual selection of registration points. Sufficient for studies with a few dozen of images Only a handful of registration points is necessary per image The key to large scale processing: Automatic determination of a few robust registration points per image Techniques such as SIFT can be useful to achieve this goal Tricky problem when dealing with ground displacement, because registration points should be selected on stable ground

19 Conclusion: The technique has broad applications and is valuable to measure many different surface processes, e.g, glacier flow, landslides, sand dunes migration, volcanoes Generally valuable to any change detection application, whenever precise co-registration of images and/or spectral bands is required (vegetation, agriculture, land monitoring, etc...) Could envision operational high resolution global monitoring of Earth surface changes using current satellite image databases for, e.g, large scale monitoring of mountainous glaciers, desertification, deforestation, etc... Optical imaging satellites have not been designed for measuring ground deformation. New applications might put new constraints on the design of future missions (tighter geometric constraints, higher image sampling, etc...)

20 Conclusion: The technique has broad applications and is valuable to measure many different surface processes, e.g, glacier flow, landslides, sand dunes migration, volcanoes Generally valuable to any change detection application, whenever precise co-registration of images and/or spectral bands is required (vegetation, agriculture, land monitoring, etc...) Could envision operational high resolution global monitoring of Earth surface changes using current satellite image databases for, e.g, large scale monitoring of mountainous glaciers, desertification, deforestation, etc... Optical imaging satellites have not been designed for measuring ground deformation. New applications might put new constraints on the design of future missions (tighter geometric constraints, higher image sampling, etc...)

21 Conclusion: The technique has broad applications and is valuable to measure many different surface processes, e.g, glacier flow, landslides, sand dunes migration, volcanoes Generally valuable to any change detection application, whenever precise co-registration of images and/or spectral bands is required (vegetation, agriculture, land monitoring, etc...) Could envision operational high resolution global monitoring of Earth surface changes using current satellite image databases for, e.g, large scale monitoring of mountainous glaciers, desertification, deforestation, etc... Optical imaging satellites have not been designed for measuring ground deformation. New applications might put new constraints on the design of future missions (tighter geometric constraints, higher image sampling, etc...)

22 Conclusion: The technique has broad applications and is valuable to measure many different surface processes, e.g, glacier flow, landslides, sand dunes migration, volcanoes Generally valuable to any change detection application, whenever precise co-registration of images and/or spectral bands is required (vegetation, agriculture, land monitoring, etc...) Could envision operational high resolution global monitoring of Earth surface changes using current satellite image databases for, e.g, large scale monitoring of mountainous glaciers, desertification, deforestation, etc... Optical imaging satellites have not been designed for measuring ground deformation. New applications might put new constraints on the design of future missions (tighter geometric constraints, higher image sampling, etc...)

23 The End: Thank you! Questions?