Precise Georeferencing of Long Strips of Quickbird Imagery Dr Mehdi Ravanbakhsh Dr Clive Fraser WALIS Forum 2013, Perth, 7-8 November

Outline Motivation for Long Strip Adjustment & Previous Experience with ALOS PRISM Sensor Sensor Orientation Models, ‘physical’ & RPCs Evaluation of Adjustments of Long QB Strips >240 km Concluding Remarks

Motivation for HRSI Strip Adjustment Previous work: Geoscience Australia (GA) generated orthorectified ALOS PRISM mosaic of Australia – Reduction of GCPs by >95% without significant loss of accuracy, ie 500 GCPs vs 30,000+ GCPs

Motivation for HRSI Strip Adjustment Previous solution: processing of individual scenes Main bottleneck in the production line: Determination of GCPs Also, a similar problem exists in mapping from HRSI in remote or hostile regions where GCPs are not easy to establish The need for strip adjustment of HRSI images

Rational functions for sensor orientation of HRSI Object-to-image space RPC transformation is from offset normalised latitude, longitude & height to offset normalized line & sample coordinates

RPCs not always available or economically accessible Task to implement sensor models for various pushbroom scanners, eg: SPOT 5, QuickBird, WorldView and ALOS / PRISM Vendors use different models for the sensor geometry even though the geometry is largely the same Vendors often provide high-quality metadata describing orbital position and interior orientation parameters RPCs are not appropriate for long image strips Development of a generic sensor model for pushbroom scanners incorporating mapping of vendor-specific definitions to the model Practical Motivation for a Generic, Rigorous Sensor Model

XOXO ZOZO YOYO Object coordinate system [X ECS ] – Earth-centred, cartesian system Orbital coordinate system [X O ] – Origin is satellite position S(t) – X O is nearly parallel to velocity v(t c ) – Y O is parallel to S(t c ) x v(t c ) – Z O is parallell to S(t c ) Platform coordinate system [X P ] – Fixed relative to the satellite – Time-dependant rotations roll(t), pitch(t), yaw(t) Rigorous Sensor Model: Coord. Systems & Transformations Satellite orbit X ECS Y ECS Z ECS EC NP XOXO ZOZO YOYO XPXP ZPZP YPYP XPXP ZPZP YPYP XPXP ZPZP YPYP XPXP ZPZP YPYP XPXP ZPZP YPYP XPXP ZPZP YPYP P ECS = S(t) + R O (t c )·P O P O = R P (t)·P P S(t)

Camera coordinate system [X C ] ̶Considers camera mount in platform system ̶Origin is projection centre C ̶X C is parallel to CCD array,Y C to focal plane Framelet coordinate system [X F ] ̶Origin is “leftmost” pixel of CCD array ̶Shifted by inner orientation (X F 0, Y F 0, f) Image file coordinate system [X I ] ̶X I = X F, Y I is the row index, t = t(0) +  t * Y I YFYF XFXF ZFZF YCYC XCXC ZCZC YPYP XPXP ZPZP P P = C M + R M ·P C P C = ·(p F -c F +  x) p I (X I, Y I, 0)  p F (X F, 0, 0) and time t XIXI YIYI X F (t) C Rigorous Sensor Model: Coord. Systems & Transformations

“Real” orbit Approximated orbit SiSi SjSj SkSk Final transformation between framelet system and ECS Correction of systematic errors in Orbit S(t) and attitudes R P (t) – S(t) and attitudes  P (t) with R P (  P (t)) modelled by cubic splines – Image observations relate to “real” orbit S(t) and attitudes  P (t) – Observed orbit points are direct observations for S(t) and  P (t) – unknown correction  S modelled as offset or time-dependant term (for path and attitudes) Bundle adjustment using ground control points and vendor-provided observations to determine the corrections P ECS = S(t) + R O ·R P (t) · [C M + ·R M ·(p F – c F +  x )] Rigorous Sensor Model: Coord. Systems & Transformations

Transformation between framelet system and ECS – Matrix R Q T transforms direct from ECS to platform system with Z-axis already pointing towards the target – Position p 0 F of framelet coordinate system and corrections dx in camera system – Substitution yields: – Transforming every discrete quaternion tuple for R Q T (t) to R P (t) delivers roll(t), pitch(t) and yaw(t) – Now fits the form of the generic model (after Z-rotations applied to p 0 F & dx) Vendor specific adjustments / Quickbird P ECS = S(t) + R Q T (t) · [C MQ + ·R MQ T · (p F +p 0 F +  x )] R M = R O ·R Q T (t c )·R MQ T R P (t) = R O T ·R Q T (t)·R Q T (t c )·R O C M = R O T ·R Q T (t c )·C MQ P ECS = S(t) + R O ·R P (t) · [C M + ·R M ·(p F – c F +  x )]

HRSI Strip Adjustment Adjustment of orbit & attitude data, essentially via ‘resection’ Use of 4-8 GCPs at strip ends only No photogrammetry tie points used in non-stereo strips Adjustment for full strip length; bias-corrected RPCs then generated for single scenes

Strip adjustment: merged camera and orbit data Pushbroom scanner scene 2 Orbit Path Orbit Attitudes Camera Mounting Camera Pushbroom scanner scene 1 Orbit Path Orbit Attitudes Camera Mounting Camera Camera Mounting Camera Orbit Path Orbit Attitudes Orbit Path Orbit Attitudes Modularised sensor model Individual scenes can share components ̶ Internal camera parameters ̶ Exterior orientation  Strip adjustment Merged Orbit Attitudes Merged Orbit Path One set of EO parameters One set of bias correction parameters per strip Bridging of scenes without ground control Camera Mounting Camera Camera Mounting Camera

Long-Strip Adjustment of Quickbird Imagery Strip 1: 16 scene 240km long Nth-Sth, constant sensor azimuth 143 GPS ground survey points Strip 2: 13 scene 188km long NE-SW, varying sensor azimuth 87 GPS ground survey points Scanned from NE to SW Strip 1 Strip 2 Melbourne

Quickbird: Strip 1 Results N Geopositioning accuracy to 1.2 pixels cross-track & 2.3 pixels along- track with only 4 endpoint GCPs GCP sets No. of CKPs RMS(E) m RMS(N) m RMS of residuals (m) Max(E) m Max(N) m Set 1: 4 GCPs Set 2: 4 GCPs Set 3: 6 GCPs Set 4: 6 GCPs Set 5: 6 GCPs Set 6: 6 GCPs; 2 x top, mid, bottom Set 7: 6 GCPs; 2 x top, mid, bottom Set 8: 20 GCPs along the strip Set 9: 20 GCPs along the strip scenes

Quickbird: Orbital Translation (bias) GCP sets Shift-X (m) Shift-Y (m) Shift-Z (m) Set 1: 4 GCPs Set 2: 4 GCPs Set 3: 6 GCPs Set 4: 6 GCPs Set 5: 6 GCPs Set 6: 6 GCPs; 2 x top, mid., bottom Set 7: 6 GCPs; 2 x top, mid., bottom Set 8: 20 GCPs along the strip Set 9: 20 GCPs along the strip Per scene bias computation Scene NoShift-X (m)Shift-Y (m) No of GCPs Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Estimated Shifts for different GCP configuration

N Quickbird: Strip 2 Results 13 scenes GCP sets No. of CKPts RMS(E) m RMS(N) m Set 1: 4 GCPs Set 2: 4 GCPs Set 3: 6 GCPs Set 4: 6 GCPs Set 5: 6 GCPs Set 6: 6 GCPs Set 7: 6 GCPs RPCs for set Set 8:6 GCPs; ; 2 x top, mid., bottom Set 9: 6 GCPs; 2 x top, mid., bottom Set 10: 20 GCPs along the strip Set 11: 20 GCPs along the strip Maximum  E &  N residuals of 13 pixels for 4 & 6 GCPs and 4 pixels for 20 GCPs

N Quickbird: Strip 2 Results 13 scenes GCP sets No. of CKPts RMS(E) m RMS(N) m Set 1: 4 GCPs Set 2: 4 GCPs Set 3: 6 GCPs Set 4: 6 GCPs Set 5: 6 GCPs Set 6: 6 GCPs Set 7: 6 GCPs RPCs for set Set 8:6 GCPs; ; 2 x top, mid., bottom Set 9: 6 GCPs; 2 x top, mid., bottom Set 10: 20 GCPs along the strip Set 11: 20 GCPs along the strip Maximum  E &  N residuals of 13 pixels for 4 & 6 GCPs and 4 pixels for 20 GCPs

Quickbird: Strip 2 Results GCP sets Shift-X (m) Shift-Y (m) Shift-Z (m) Set 1: 4 GCPs Set 2: 4 GCPs Set 3: 6 GCPs Set 4: 6 GCPs Set 5: 6 GCPs Set 6: 6 GCPs Set 7: 6 GCPs Set 8:6 GCPs; 2 x top, mid., bottom Set 9: 6 GCPs; 2 x top, mid., bottom Set 10: 20 GCPs along the strip Set 11: 20 GCPs along the strip Scene No Shift-X (m) Shift-Y (m) No of GCPs Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Scene Per scene bias computation Estimated Shifts for different GCP configuration

Strip adjustment can achieve reduction of GCPs by >95% without signifcant loss of accuracy Concept proven at Geoscience Australia & led to automated production of AGRI Results thus far are better for non-agile satellites than for steerable HRSI systems Concluding Remarks on Long Strip Adjustment

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