PRISM geometric Cal/Val and DSM performance

Transcription

1 PRISM geometric Cal/Val and DSM performance Junichi Takaku RESTEC Takeo Tadono JAXA Nov. 2008

2 Contents PRISM geometric Cal/Val Interior orientation parameters Exterior orientation parameters Triangulation accuracies DSM performance Test sites and scenes PRISM DSM generation Adaptive Correlation Filter Summary 1

3 PRISM geometric Cal/Val Interior orientation parameters (CCD alignment data) 6 CCD units for NDR / 8 CCD units for FWD& BWD Approx. 5,000 detectors on each CCD Interior orientation parameters are described by the CCD units alignment data on the theoretical CCD alignment plane Calibrated by on-orbit self-calibrations BWD Pixel No.1 CCD1 Pixel No.1 CCD2 Flight Direction ALOS / Focal Point NDR CCD1 CCDA/B CCD2 CCD1 CCD1 CCD3 CCD2 CCD2 CCD4 CCD3 CCD3 CCD5 CCD4 CCD4 CCD6 CCD5 CCD5 CCD6 CCD6 CCD7 CCD8 Earth rotate direction Forward Nadir PRISM CCD units configuration Ground/ CCD Plane CCD7 CCD8 Backward FWD Schematic views (emphasized) of CCD alignments 2

4 PRISM geometric Cal/Val GCP residuals sigma calculated by image orientations as the accuracies of CCD alignment data Last update of CCD alignment data was performed on July 2007 GCP Residuals Sigma on L1B1 [pixel] GCP Residuals Sigma on L1B1 [pixel] GCP Residuals Sigma on L1B1 [pixel] / 01'06 08/ 01'06 12/ 01'06 04/ 01'07 08/ 01'07 12/ 01'07 04/ 01'08 08/ 01'08 NDR Scene 181 Date scenes X 1.5 Y / 01'06 08/ 01'06 12/ 01'06 04/ 01'07 08/ 01'07 12/ 01'07 04/ 01'08 08/ 01'08 BWD Scene 174 Date scenes X 1.5 Y FWD 120 scenes / 01'06 08/ 01'06 12/ 01'06 04/ 01'07 08/ 01'07 12/ 01'07 04/ 01'08 08/ 01'08 Scene Date Dates trends (2.5 years) of GCP residuals sigma on L1B1 (6~230 GCPs on each scene) X Y 3

5 PRISM geometric Cal/Val Exterior orientation parameters Co-linear function X Y Z = X Y Z 0 0 ( t) x ( t) t y + λμ( ) ( t) c 0 I where, [X,Y,Z] t [x,y,-c] t [X0(t),Y0(t),Z0(t)] t M(t) t λ I = GCP in object space = CCD coordinates of GCP defined with CCD alignment data = sensor position given by satellite GPSR data = rotation between CCD coordinates and object space = time of image line = scale factor = FWD/NDR/BWD 4

6 PRISM geometric Cal/Val Exterior orientation parameters Co-linear function M( Μ t) = Μ Μ ( t) Μ ph att ei 0I where, M0I Matt(t) Mph MeI M ei = nominal static alignment rotations between CCD coordinates and attitude reference coordinates (satellite Roll, Pitch and Yaw axis) = satellite attitude (i.e. STT and IRU) data which gives the rotation from attitude reference coordinates to object space = physical effect compensation (e.g. atmospheric distortion: fixed rotations) = an error to be estimated as unknown parameters cosθ 0 sinθ = cosφ sinφ sinθ 0 cosθ 0 sinφ cosφ STT Pitch Yaw Roll Only roll (φ) and pitch (θ) errors should be estimated as unknowns!! They have time depending (not inside the scene) error trends because those are caused by the sensor alignment changes depending on satellite thermal conditions NDR BWD PRISM configuration FWD 5

7 PRISM geometric Cal/Val Exterior orientation parameters Time depending error trend model of roll and pitch angles Short term trend - orbit cycle s by 2nd degree Fourier series Long term trend - observing date d by linear model 2 φei ( d, s) = ci 0 + ci1 d + ( aφ Ik cos 2πks + bφ Ik sin 2πks) where, ci0, ci1 aφik, bφik k = 1 = long term linear parameters = short term Fourier series parameters d = total days from Jan.1, 2006 s = ratio of time from opening of satellite eclipse to orbit cycle period Tsc -Tec Tec Sun Tsc Scene center Opening of satellite eclipse S = T sc T T ec Earrh Center Orbit cycle T Orbit cycle parameter s 6

8 PRISM geometric Cal/Val Exterior orientation parameters Latest Fitting model released by July 2008 The Pitch trend model was updated by d=731 (Jan.2, 2008) because the error trends was obviously changed Roll Error [deg] Roll Error [deg] s d Pitch Error [deg] Pitch Error [deg] s d Fitting models New fitting models of Pitch Samples for fitting models Samples for new fitting model of Pitch Fitting result of Roll/Pitch error trend of FWD 134 scenes as of Sep.15,

9 PRISM geometric Cal/Val Exterior orientation parameters Latest Fitting model released by July 2008 The Pitch trend model was updated by d=731 (Jan.2, 2008) because the error trends was obviously changed Roll Error [deg] Roll Error [deg] s d Pitch Error [deg] Pitch Error [deg] s d Fitting models New fitting models of Pitch Samples for fitting models Samples for new fitting model of Pitch Fitting result of Roll/Pitch error trend of NDR 222 scenes as of Sep.15,

10 PRISM geometric Cal/Val Exterior orientation parameters Latest Fitting model released by July 2008 The Pitch trend model was updated by d=731 (Jan.2, 2008) because the error trends was obviously changed Roll Error [deg] Roll Error [deg] s d Pitch Error [deg] Pitch Error [deg] s d Fitting models New fitting models of Pitch Samples for fitting models Samples for new fitting model of Pitch Fitting result of Roll/Pitch error trend of BWD 203 scenes as of Sep.15,

11 PRISM geometric Cal/Val Exterior orientation parameters Latest Fitting model released by July 2008 Trend model fitting residuals (can be regarded as bias errors for scenes without GCP) No. of Residuals (sigma) ground level [m] Scenes Roll Pitch CT AT FWD NDR BWD

12 PRISM geometric Cal/Val Triangulation accuracies by triplet stereo images Triangulation XYZ bias errors of 123 site scenes without GCPs estimated by fitting residuals of roll and pitch error trends of triplet stereo images Approx. 4~5m RMSE for X/Y Approx. 13m RMSE for Z X Y Z Bias Error [m] / 01'07 07/ 01'07 10/ 01'07 01/ 01'08 04/ 01'08 07/ 01'08 10/ 01'08 Dat e Estimated bias errors of triangulations vs. observation dates 123 sets of triplet stereo images 11

13 New DSM validation test sites Three small LiDAR sites in Japan One large LiDAR site from Puget Sound Lidar Consortium (PSLC) The public-domain high resolution LiDAR data for Puget Sound region in Washington, USA via web site located at: Site Size Reference LiDAR-DSM data sites Height Range Ground Resolution Height Accuracy Source Year Mt.Tukuba 1.5x1.5km 200m 1m 0.8m 2004 Chiriin 1.5x1.5km 50m 1m 0.8m 2004 Mt.Ibuki 1.2x1.9km 700m 1m 0.8m 2005 Puget Sound 50x70km *1) 500m *1) 1.8m 0.3m *2) *1) for our target area, *2) in flat open surface PRISM test scenes (triplet stereo) No. Ref. DSM Sites Obs. Date No. of GCPs No. of TPs 1 Mt.Tukuba/Chiriin 03/01/ Mt.Ibuki 09/11/ Puget Sound 09/12/

14 DSM validation test sites Locations of PRISM test scenes including DSM sites Scene no.3 Scene no.4 Additional LiDAR DSM sites in Japan Puget Sound LiDAR site in Washington, USA (Map from PSLC web site) Green areas represent data available areas and blue squares represent PRISM scene coverage 13

15 Triangulation accuracies of scene no.1 and no.2 XY accuracies are <3m RMSE without GCP for both samples Z accuracies are 18m RMSE for no.1 and 1m RMSE for no.2 without GCP Only 1 GCP improve the Z accuracy of no.1 to <3m RMSE RMSE of ICP [m] RMSE- xy for no.1 RMSE- xy for no.2 RMSE- z for no.1 RMSE- z for no Number of GCP RMSE of ICPs for scene no.1 and no.2 14

16 DSM generation Processed by EORC DSM/ORI (Ortho Rectified Image) Generation Software for ALOS PRISM (DOGS-AP). DSM grid is 10m (4 pixels) in image frame, then resampled to 0.3 arc-sec geodetic latitude-longitude frame. Reference DSM is resampled to the same frame (0.3 arc-sec geodetic latitude-longitude) and height difference is calculated. Water (i.e. sea, large liver/lake) areas are masked as dead areas. No manual correction on generated DSM 15

17 DSM performance Scene 1 : Reference DSM site (Mt.Tsukuba Reference DSM site (Chiriin 35km NDR imageprism-dsm in 0.3 arc-sec Lat-Lon frame 16

18 DSM performance Scene 2 : Reference DSM site (Mt.Ibuki 35km NDR image PRISM-DSM in 0.3 arc-sec Lat-Lon frame 17

19 DSM height accuracies Three small sites in scene no.1 and no.2 Height Error Standard Deviation [m] with GCPs without GCP Chiriin Mt.Tsukuba Mt.Ibuki Flat Mountainous Steep Sit e Standard Deviations Height Error Bias [m] with GCPs without GCP Chiriin Mt.Tsukuba Mt.Ibuki Flat Mountainous Steep Sit e Bias errors DSM height accuracy statistics Site Terrain GCP Points Bias [m] SD [m] RMSE [m] Max [m] Min [m] Mt.Tsukuba Mountainous Chiriin Mt.Ibuki Flat Steep

20 Mt.Tsukuba PRISM-DSM NDR image (1.8kmx2.2km) DSM difference LiDAR-DSM image of Mt.Tsukuba (PRISM-DSM (1.8kmx2.2km) minus LiDAR) 19

21 Chiriin PRISM-DSM NDR image (1.8x2.0km) DSM difference LiDAR-DSM image of Chiriin (PRISM-DSM (1.8x2.0km) minus LiDAR) 20

22 Mt.Ibuki PRISM-DSM NDR image (1.5x2.2km) DSM difference LiDAR-DSM image of Mt.Ibuki (PRISM-DSM (1.5x2.2km) minus LiDAR) 21

23 Scene 3: Scene 4 : DSM 35km in NDR 0.3 arc-sec image Lat-Lon frame 35km DSM in NDR 0.3 arc-sec imagelat-lon frame Mosaic PRISM-DSM (35km x 60km) 22

24 City 1 Forest 1 Forest 2 City 2 DSM difference image of Mosaic DSM (PRISM-DSM minus LiDAR) PRISM-DSM in 0.3 arc-sec Lat-Lon frame 23

25 Forest 2 (vegetation area) NDR image PRISM DSM PSLC DSM PRISM minus PSLC 24

26 City 1 (non-vegetation area) NDR image PRISM DSM PSLC DSM PRISM minus PSLC 25

27 DSM height accuracies PSLC site for scene no.3 and no Bias RMS Errors [m] Whole Forest1 Forest2 City1 City2 DSM height accuracy statistics Area Points Bias [m] SD [m] RMSE [m] Max [m] Min [m] Whole Forest Forest City City

28 Adaptive Correlation Filter (ACF) for DSM Triplet stereo matching algorithm on epipolar frame Two cross correlations of FWD-NDR (F-N) and BWD-NDR (B-N) are simultaneously calculated Parallax direction (epipolar frame) BWD NDR FWD ρ = 1 N N i = ( x i x )( y i y ) 1 σ x σ y sum ρ = 1 N N i = ( x i x )( y i y ) 1 σ x σ y Cross correlation Cross correlation 27

29 Adaptive Correlation Filter (ACF) for DSM Excludes the suspicious matching grid point depending on the combination of two cross correlations Thresholds of correlations are independently set for F-N and B-N respectively The combinations, AND / OR, of those thresholds are alternative AND: Points which the both of two correlations satisfy each threshold survive and other points are filtered OR: Points whether F-N or B-N correlation satisfies each threshold survive Filtered area by 0.7 AND OR 0.7 If ( CF-N(p) > TF-N p survives; else p is filtered; AND/OR CB-N(p) > TB-N) p = matching grid points CF-N(p) = F-N correlation of p CB-N(p) = B-N correlation of p TF-N = F-N correlation threshold TB-N = B-N correlation threshold F-N correlation 0.0 Correlation 1.0 B-N correlation 28

30 Adaptive Correlation Filter (ACF) for DSM Trend of RMSE and maximum errors of ACF applied DSM with various thresholds and combinations at various local terrain areas Threshold 0.5 to 0.7 and OR combination seemed to be suitable for the accuracy improvement Height RMSE [ m] Height Max Error [m] 60.0 Paddy Paddy2 Cit y Cit y2 Cit y3 Cit y Farm1 Forest Farm2 Farm Forest2 Forest3 Paddy1 Paddy2 Cit y1 Cit y2 Cit y3 Cit y4 Farm1 Forest1 Farm2 Farm3 Forest2 Forest3 0.0 None 0.0 or 0.0 and 0.3 or 0.3 and 0.5 or 0.5 and 0.7 or 0.7 and 0.9 or 0.9 and 0.0 None 0.0 or 0.0 and 0.3 or 0.3 and 0.5 or 0.5 and 0.7 or 0.7 and 0.9 or 0.9 and RMSE trend of DSM with ACF Max-error trend of DSM with ACF 29

31 Adaptive Correlation Filter (ACF) for DSM DSM height accuracies comparison between ACF applied and not applied with the threshold 0.7 OR 0.7 at DSM sites RMSE [m] None Applied 4.50 Saitama Thun SW Bern DSM accuracies comparison between ACF applied and not 30

32 Adaptive Correlation Filter (ACF) for DSM ACF results in a low texture (paddy) area with threshold 0.7 OR 0.7 pre-filtered DSM nadir image 0.0 Correlation 1.0 F-N correlation post-filtered DSM filtered area on nadir image 0.0 Correlation 1.0 B-N correlation 31

33 Summary Geometric Cal/Val The accuracies of interior parameters were confirmed to be kept within 0.7 pixels on image coordinates for all triplet images. The trend models of exterior parameters were updated and the residuals of those models i.e. automatic geo-location accuracies were approx. 4m sigma in CT bias and 5~7m sigma in AT bias for all triplet images. DSM performance The DSM height accuracies were validated on new LiDAR sites. The accuracies of three small sites in Japan were 4~6m in standard deviations. The accuracies of large PSLC sites without GCP were 9m in RMSE and almost large errors were focused on vegetation areas. In nonvegetation city areas, the accuracies were 3m in RMSE. New adaptive DSM filter for our unique triplet image matching algorithm was introduced and its efficiency was confirmed with the accuracy assessment using DSM reference sites. 32

34 PRISM geometric Cal/Val GCP site by ESA Common GCP data set to confirm the geometric accuracies of products mutually Triangulation accuracies of triplet data set observed at Nov.14, Data set coverage (on a map from AUIG 3.0) GCP distribution on nadir image (13 GCPs in L1B1/CCD merged image) 33

35 PRISM geometric Cal/Val GCP site by ESA The RMSE xy and z were approx. 5m and 13m without GCP. Only 1 GCP improves both of planimetric and height accuracies to <3m in RMSE XY Z m RMSE of ICPs [m] Number of GCP RMSE of ICPs in ESA GCP site Object space residuals in all GCP used model Circles represent the GCP location. Red bars represent the xy residual vectors. Blue bars represent the z residual magnitudes. 34