IGARSS11_takaku_dsm_report.ppt

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  • PRISM which stands for Panchromatic Remote-sensing Instrument for stereo mapping was launched in January 2006 as one of the onboard sensors of ALOS satellite. Unfortunately the ALOS ended its over 5 year mission life this April but it left us a lot of earth observing data all over the world with this three push-broom scanners, nadir, plus/minus 23.8 degree for stereo triplets in 35km swath. The panchromatic images had 2.5m ground resolution in 8bit quantization.
  • The primal purpose of PRISM mission was to create global terrain models by using triangulation techniques with the triplet stereo images. We developed DOGS-AP which stands for DSM and Ortho-rectified image Generation Software for ALOS PRISM for the objective. This is an exclusive triplet-image-matching software based on traditional photogrammetric techniques and we are processing Digital Surface Models with it. The grid spacing is set to 10m. The processing unit is 35km square scene.
  • This slide shows the visual comparison of various terrain models which have different grid spacing. SRTM-3, ASTER-GDEM, PRISM-DSM, and LiDAR-DSM which have the grid spacing of 90m, 30m, 10m, and 1m respectively. SRTM and ASTER-GDEM are already distributed to public as world global terrain data. PRISM-10m-DSM aims to provide more detailed data than those existing data set, hopefully, the one closer to the airborne LiDAR-DSM.
  • The software fully supports the PRISM sensor model which consists of interior and exterior parameters. The interior parameters are the PRISM CCD unit alignment models and we actually calibrated and validated these parameters not only for this software but also for JAXA’s standard product processor. The exterior parameters consists of PRISM sensor alignment models and satellite orbit and attitude data. These parameters make the image orientation process very easy and precise. It also contributed to the absolute accuracy of DSM processed without any GCP.
  • The image matching is based on a conventional area-based grid matching with cross correlations. We applied an unique simultaneous triplet image matching algorithm for the PRISM triplet images. The image matching is performed on the combination of two correlations, between forward and nadir and between backward and nadir. We tested various combinations of images for the triplet images in the development phase and found that this combination was most robust against blunders.
  • Some people often ask me a question why it generates the 10m grid spacing DSM while the pixel spacing of original stereo images is 2.5m. The spatial resolution of DSM depends on not only the resolution of source image, but also the sensitivity in parallax-detections in the image matching process. On the other hand, there’s always trade-off between the data resolution and their volume. So we tested a variety of different resolutions of DSMs and found that the 10m grid was most suitable in the traditional trade-off.
  • The software is being used not only at JAXA/EORC, but also at RESTEC and former ALOS-Data-Node agencies. The height accuracies from our past validations with LiDAR reference is shown here. About 3 to 5m-sigma for flat, urban, and mountainous area. About 7m-sigma in various terrain mixture area. And about 9m-sigma in vegetation area.
  • We are still processing the DSMs routinely in scene-basis with huge archive of the PRISM raw images. There are two types of DSM, namely, absolute-DSM and relative-DSM which was processed with GCP and without GCP respectively. We already processed over thirteen hundreds absolute-DSMs and forty two hundreds relative-DSMs so far. However, the processing areas are limited to our domestic area, Japan, and some other areas which have some special demands such as a sensor calibration, disaster monitoring, and so on.
  • Therefore, we are creating the global data set for Japan first. This figure shows a status of stacking archive DSM scenes in Japan as of this March. We need about 1000 scene-frames for covering whole Japan areas. 14% of them are processed with more than five scenes already and colored by green as well as 52% and 34% of them are processed by one or two and three or four scenes and colored by orange and yellow respectively. We still have missing two frames because of cloud covers. Also the frames covered with only one or two scenes may have missing areas due to cloud masks. In the PRISM mission, the actual recurrent cycle of the stereo observation was 92 days. So we had about 4 chances a year and total 20 chances in the 5 year mission for covering each frame. But usable scenes was limited like this almost due to the cloud covers. So, maybe, we should have adopted more flexible observations such as more short revisit orbit or more large roll pointing ability. This is one of the important lessons we learned for a next mission design.
  • In a mosaic processing, we stack the archive DSM-scenes on a same path-frame with a quality control and assurance process at first. And then, mosaic these stack-DSM on 1degree tiles. And next, we perform a boundary smoothing process among 1degree tiles and interpolate data on land-water or sea mask areas.
  • DSM data staking is performed by averaging DSM scenes of different dates only with height bias adjustments. In this process, partial cloud masks are filled and random noise is reduced with the averaging.
  • This table shows the height accuracy comparison between scene-DSM and stacked-DSM. The accuracies were improved at 60 to 70 centimeters in standard-deviations with averaging scenes.
  • A relative DSM has sufficient accuracy in planimetry whereas it has un-negligible height offset bias, thanks to the exterior parameters of PRISM sensor model. So, all we need is only Z-offset-bias-corrections of relative DSMs for the mosaicking; they are simply performed by using some absolute reference such as SRTM or using the average of height differences on an overlap area of a neighboring absolute DSM in a scene-by-scene basis.
  • There are one-hundred and one mosaicking 1-degree tiles which covers whole Japan.
  • We preliminary mosaicked 73 tiles of them which covers almost whole Japan except for some isolated islands. These red areas indicate the cloud masks. They are focused on the northern mountainous areas where the weather is always unstable and is seldom fine besides heavy snow in winter season. The offset bias of relative-DSMs were corrected with absolute-DSMs or height on coast lines.
  • This is the number of data stack. We used total 2265 scenes for this mosaicking process and the maximum stack-scenes was 9 while the maximum data stack was 23.
  • We evaluated the absolute height accuracies of the mosaicked DSM with independent GCPs. This is the GCP distributions. Over 3000 GCPs were used. This graph shows the GCP height vs. PRISM-DSM height. There is no systematic error trend along with the GCP height range of about 1600m. The error stats is shown here. The RMS error is about 3m and enough consistent with our past validations.
  • These images show the height-difference-images between PRISM-DSM and GSI-50m and between PRISM-DSM and SRTM-3, respectively for Kanto area of Japan. The GSI-50m DEM was derived from contours of 1/25000 map. In both difference-images, no gap or systematic errors were found in the boundaries of tiles or scenes. The height-differences are focused on vegetation on mountainous areas and a few metropolitan areas in both images, however, the trend are different between these two images. The positive errors in left image represent the height of trees and height of buildings since the GSI-50m is bare-earth DEM. These relative height data are expected to be utilized for various fields such as forestry, city planning, environmental study, and so on. The almost negative errors in right image represent pure height difference in vegetation area since the SRTM is DSM as well. It also implies the trees’ heights of PRISM-DSM tends to be lower than the ones of SRTM-3-DSM; the trend may be derived from difference of their observation dates of source data, their grid spacing, or their basic algorithms.
  • These are visual comparisons among some terrain models. PRISM-DSM 10m, digital map 10m grid by GSI Japan, ASTER-GDEM 30m, and SRTM-3 90m. We can easily confirm the difference of their ground resolutions between 10m and others and also the difference between DSM and DEM.
  • This is another part of comparison between PRISM-DSM and Digital map10m derived from 1/25000 scale map. There is obvious difference here in a mountainous area and the further inspection revealed that a new university’s structure was built two years ago with leveling down the ridges. So we can update those data easily with PRISM-DSM and can compare the differences for some applications.
  • Lastly, I will introduce another area which we are processing PRISM-DSM mosaic. The area is Bhutan Himalayas and the data is for a contribution to the study on GLOF. GLOF stands for Glacial Lake Outburst Flood. The project is led by Japanese governmental agencies. The objective of project is briefly, to extract dangerous glacial lakes which have a possibility of GLOF, to make an inventory of expanding history for glacial lakes, and to make a flood hazard map. And the PRISM-DSM is expected to be utilized on these objectives. There were two related presentations already made by my colleagues in this conference.
  • Bhutan is here in Himalayas. Thirteen 1-degree tiles cover the whole Bhutan areas for the DSM mosaic.
  • This is the AVNIR-2 image mosaic on the areas. There are a lot of heavy snow areas because of their very high altitude.
  • This figure shows the SRTM-3 data which cover the tiles. The height range is more than 7000 meters there in Himalayas. There are a lot of void areas indicated in red in this figure because of the world steepest terrain.
  • This is the mosaicked PRISM-DSM we provided to the project. These red areas are the cloud and snow masks and no-data areas. The data covers almost void areas of SRTM while the SRTM covers the mask areas of PRISM-DSM. So we can fill the missing areas each other. These are the boundaries of 1-dgree tiles.
  • This is the number of data stack in the mosaicking. Total 160 scenes of PRISM-DSM were used in the mosaic and the maximum data stack was 11. The offset-bias of relative-DSM to be corrected were estimated from the mode of difference from SRTM-3.
  • This is the difference image between the PRISM-DSM and SRTM-3. There are no systematic errors on the boundary between scenes or the one between tiles. These random errors are derived from the difference of their spatial resolutions.
  • This is a visual comparison in the shaded relief of DSMs between PRISM-DSM and SRTM-3 on one of the target glacial lakes which I showed before. We can easily confirm the difference of their resolutions and void areas.
  • This is the one for another area. There is no quantitative result or actual applications to the project in my presentation, however we expect that the data will contribute to the project with its high spatial resolution and small missing areas compared with the only existing SRTM data.
  • IGARSS11_takaku_dsm_report.ppt

    1. 1. High Resolution DSM Generation from ALOS PRISM - Archiving and Mosaicking - Junichi Takaku RESTEC July 2011 Takeo Tadono JAXA
    2. 2. Contents <ul><li>Introduction </li></ul><ul><li>Software overview </li></ul><ul><li>Data archiving status </li></ul><ul><li>DSM mosaic for global data set </li></ul><ul><li>Summary </li></ul>
    3. 3. Introduction <ul><li>Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard Advanced Land Observation Satellite (ALOS) </li></ul><ul><ul><li>Mission Life: Jan.2006. – Apr.2011 </li></ul></ul><ul><ul><li>Three push-broom scanners, nadir, +23.8 ゚ & –23.8 ゚ for stereo triplets in 35km-swath. </li></ul></ul><ul><ul><li>Pan images: 2.5 m, 8 bit. </li></ul></ul>© JAXA © JAXA
    4. 4. Introduction <ul><li>Digital Surface Model (DSM) generation from ALOS PRISM </li></ul><ul><ul><li>with DOGS-AP (DSM and Ortho-rectified image Generation Software for ALOS PRISM) – JAXA’s exclusive triplet-image-matching software </li></ul></ul><ul><ul><li>Grid spacing: 10m (0.3 arc-sec) </li></ul></ul><ul><ul><li>Unit: 35km x 35km / scene </li></ul></ul>PRISM Triplet stereo images DSM (and Ortho-image) DOGS-AP
    5. 5. Introduction <ul><li>Visual Comparison (4km x 4km) </li></ul>SRTM-3 DEM ASTER-GDEM PRISM-DSM LiDAR-DSM 0m 150m 90m N60 ゚ ~S56 ゚ PRISM data areas except for clouds N/A IfSAR from STS-99 Airborne LiDAR Mesh Size Data Area Source N83 ゚ ~S83 ゚ Satellite Stereo Triangulation ASTER GDEM is a product of METI and NASA 30m 10m 1m Satellite Stereo Triangulation
    6. 6. Software overview <ul><li>Full support for the PRISM sensor model </li></ul><ul><ul><li>Interior parameters </li></ul></ul><ul><ul><li>Exterior parameters (including satellite orbit and attitude data) </li></ul></ul><ul><ul><li>Relative / absolute orientation </li></ul></ul>PRISM CCD unit alignments PRISM sensor alignments on optical bench (side view) FWD BWD NDR STT Optical Bench Roll Yaw Pitch
    7. 7. Software overview (cont.) <ul><li>Area-based grid matching with cross-correlations </li></ul><ul><li>Simultaneous triplet image matching (F-N & B-N) </li></ul>FWD(F) NDR(N) BWD(B) DSMs generated with different stereo pairs F-N & B-N B-N F-N F-B F-N & B-N & F-B
    8. 8. Software overview (cont.) <ul><li>Why 10m grid spacing? </li></ul>PRISM-DSMs generated in different grid spacing PRISM nadir image 2.5m 20m 10m 5m 2.5m 100m 0m Height Scale
    9. 9. <ul><li>Being used at JAXA, RESTEC, and former ALOS-Data-Node agencies (ASF/America, GSTDA/Thailand, and GA/Australia) </li></ul><ul><li>Accuracies from our past validations with LiDAR reference </li></ul>Software overview (cont.) *1) provided by Geospatial Information Authority of Japan (GSI) *2) provided by Alaska Satellite Facility (ASF) -75 83 2.88 2.82 0.60 43669079 Kenai-W *2 Flat & Mountain -237 164 8.75 8.70 0.94 23465889 PSLC Vegetation -73 32 5.21 4.92 -1.70 1287801 Tsukuba Mountainous -178 83 5.09 4.88 1.47 1505512 Saitama *1 Flat & Urban Kenai-E *2 Fukuoka *1 Site -102 112 5.96 5.69 -1.80 30327521 Steep -210 117 7.39 7.05 2.20 1178376 Various Min [m] Max [m] RMSE [m] SD [m] Bias [m] Points Terrain
    10. 10. Data archiving status <ul><li>Scene-DSM archives </li></ul><ul><ul><li>Absolute-DSM (GCP used) : >1300 scenes </li></ul></ul><ul><ul><li>Relative-DSM (GCP not used) : >4200 scenes </li></ul></ul>(as of June 2011) Locations of scene-PRISM-DSMs processed in JAXA/EORC as of June 2011
    11. 11. <ul><li>Stacking scene-DSMs in Japan (as of Mar. 2011) </li></ul>Path : 57~92   (pt. +/-1.2 for each) Frame : 2680~3115 1016 scene-frames for covering whole Japan No. of stack scenes for each frame Data archiving status (cont.) 34.1 347 3~4 Color 13.7 139 5~9 52.0 528 1~2 0.2 2 0 % No. of frames No. of stacks 90 Path 80 70 60 2700 2800 2900 3000 3100 Frame
    12. 12. <ul><li>Mosaic processing </li></ul>Scene-DSM in 0.3 arc-sec (path-frame) ... DSM mosaic for global data set Boundary smoothing among 1 ゚ x1 ゚ tiles and interpolating data on land-water/sea masks Mosaic-DSM on 1 ゚ x1 ゚ tile Mosaicking on 1 ゚ x1 ゚ tile Stack-DSM QC/QA and stacking on same path-frame
    13. 13. <ul><li>DSM data stacking </li></ul>Scene-DSMs of different dates DSM mosaic for global data set (cont.) Cloud masks Noise in low correlation areas <ul><li>Averaged only with height bias adjustments </li></ul><ul><li>Filling holes of cloud masks </li></ul><ul><li>Noise reduction </li></ul>Stack-DSM
    14. 14. <ul><li>DSM data stacking </li></ul>DSM mosaic for global data set (cont.) Accuracy comparison between scene-DSM and stack-DSM Difference from LiDAR reference -210 117 7.39 7.05 2.20 1 Fukuoka Various -96 36 4.37 4.17 -1.30 5 -189 63 4.39 4.14 1.44 4 3 1 1 Stack scenes -73 32 5.21 4.92 -1.70 Tsukuba Mountainous -178 83 5.09 4.88 1.47 Saitama Flat & Urban Site -206 89 6.63 6.40 1.73 Min [m] Max [m] RMSE [m] SD [m] Bias [m] Terrain
    15. 15. <ul><li>Mosaicking on 1 ゚ x1 ゚ tiles </li></ul><ul><ul><li>Offset-bias errors in relative-DSMs: </li></ul></ul><ul><ul><ul><li>X : 5m RMS </li></ul></ul></ul><ul><ul><ul><li>Y : 4m RMS </li></ul></ul></ul><ul><ul><ul><li>Z : 14m RMS </li></ul></ul></ul><ul><ul><li>Only Z offset-bias of relative-DSMs were corrected with absolute reference (e.g., SRTM) or the average of height differences on an overlap area of a neighboring absolute DSM. </li></ul></ul>Negligible for 10m grid spacing DSM mosaic for global data set (cont.) Absolute-DSM Relative-DSMs Z-Offset-bias correction
    16. 16. <ul><li>1 ゚ x1 ゚ tiles in Japan (101 tiles) </li></ul>DSM mosaic for global data set (cont.) 145 140 135 130 125 45 40 35 30 25
    17. 17. <ul><li>1 ゚ x1 ゚ tiles mosaic in Japan </li></ul><ul><ul><li>73 tiles of all 101 tiles </li></ul></ul><ul><ul><li>Offset bias of relative-DSMs were corrected with absolute-DSMs or heights on coast lines </li></ul></ul>DSM mosaic for global data set (cont.) Gray scale image of mosaicked DSM (Preliminary result) >2000m 0m Cloud / Snow Land-Water Sea Mask: Height scale (Preliminary)
    18. 18. DSM mosaic for global data set (cont.) Number of Data Stack <ul><li>1 ゚ x1 ゚ tiles mosaic in Japan </li></ul><ul><ul><li>Total 2265 scene-DSMs </li></ul></ul><ul><ul><ul><li>647 absolute-DSMs </li></ul></ul></ul><ul><ul><ul><li>1618 relative-DSMs </li></ul></ul></ul><ul><ul><li>Max. 9 scenes stack </li></ul></ul><ul><ul><li>Max. 23 data stack </li></ul></ul>>10 0 Data stack (Preliminary)
    19. 19. <ul><li>Height accuracies on GCPs </li></ul>DSM mosaic for global data set (cont.) GCP distribution (3236 GCPs) GCP heights vs. PRISM-DSM heights (GCP height range: 11m~1567m) Height difference statistics (in meters) Height difference histogram -20.97 36.70 3.36 3.33 -0.47 Min Max RMS  
    20. 20. <ul><li>Height difference from existing global terrain data </li></ul><ul><ul><li>GSI50m-DEM: 50m-DEM derived from contour lines of 1/25000 map </li></ul></ul><ul><ul><li>SRTM-3: 90m-DSM derived from C-band IfSAR </li></ul></ul>DSM mosaic for global data set (cont.) +30m -30m +/-0m +30m -30m +/-0m Height difference from SRTM-3 for 3x3 tiles in Kanto-region Height difference from GSI50m-DEM for 3x3 tiles in Kanto-region
    21. 21. <ul><li>Visual comparison (6km x 6km) </li></ul>DSM mosaic for global data set (cont.) PRISM-DSM (10m) Digital Map 10m Grid (Elevation) by GSI ASTER-GDEM (30m) ASTER GDEM is a product of METI and NASA SRTM-3 (90m) Derived from contour lines of 1/25000 map 0m 200m
    22. 22. <ul><li>Visual comparison (3km x 3km) </li></ul>DSM mosaic for global data set (cont.) PRISM-DSM (10m) Digital Map 10m Grid (Elevation) by GSI Kyushu Univ. / Ito campus Built in 2009
    23. 23. <ul><li>Contribution to the study on GLOF in Bhutan Himalayas </li></ul><ul><ul><li>GLOF (Glacial Lake Outburst Flood) project supported by JICA/JST </li></ul></ul><ul><ul><ul><li>To extract dangerous glacial lakes which have a possibility of GLOF. </li></ul></ul></ul><ul><ul><ul><li>To make an inventory of expanding history for glacial lakes. </li></ul></ul></ul><ul><ul><ul><li>To make a flood hazard map. </li></ul></ul></ul>DSM mosaic for global data set (cont.) WEP.M.6 “ DEVELOPMENT OF GLACIAL LAKE INVENTORY IN BHUTAN USING “DAICHI” (ALOS)” By Takeo Tadono, et. al. TH1.T04.4 “ TEMPORAL MONITORING OF SUPRAGLACIAL LAKES ON TSHOJO GLACIER AT BHUTAN” By Tsutomu Yamanokuchi, et. al. Related presentations in IGARSS2011 Utilizing PRISM-DSM Glacial lakes in Bhutan on AVNIR-2 3D-view image with PRISM-DSM
    24. 24. DSM mosaic for global data set (cont.) N26 N27 N28 N29 E88 E89 E90 E91 E92 E93 PRISM-DSM mosaic tiles over Bhutan area <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>N026E088 N026E089 N026E090 N026E091 N026E092 N027E088 N027E089 N027E090 N027E091 N027E092 N028E089 N028E090 N028E091
    25. 25. DSM mosaic for global data set (cont.) AVNIR-2 image mosaic over Bhutan <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>
    26. 26. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) SRTM-3 (version 2) Void data in SRTM-3 7000m 0m Height scale
    27. 27. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) 7000m 0m Height scale PRISM-DSM mosaic (0.3 arc-sec) N026E088 N026E089 N026E090 N026E091 N026E092 N027E088 N027E089 N027E090 N027E091 N027E092 N028E089 N028E090 N028E091
    28. 28. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) Number of data stack (max 11 data in 160 scenes mosaic) >5 0 Data count Offset-bias of relative-DSMs were estimated from the mode of difference from SRTM-3
    29. 29. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) +30m -30m PRISM-DSM minus SRTM-3 +/-0m Void data in SRTM-3
    30. 30. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) PRISM-DSM (0.3 arc-sec / 10m) SRTM-3 (3.0 arc-sec / 90m) voids mask Visual comparison (5km x 5km)
    31. 31. <ul><li>Contribution to a study on GLOF in Bhutan Himalayas </li></ul>DSM mosaic for global data set (cont.) PRISM-DSM (0.3 arc-sec / 10m) SRTM-3 (3.0 arc-sec / 90m) voids Visual comparison (10km x 10km)
    32. 32. Summary <ul><li>Processed thousands of PRISM-scene-DSMs in 10m-grid so far. </li></ul><ul><li>Tested over 70 1 ゚ x1 ゚ tiles of their seamless mosaic in Japan area. </li></ul><ul><li>Processed 13 1 ゚ x1 ゚ tiles of their seamless mosaic in very steep Bhutan Himalayas and provided them to the GLOF project. </li></ul><ul><li>Planning to complete a global DSM mosaic for the whole areas of Japan, first. </li></ul>PRISM-scene-DSM is provided to general users by RESTEC as a value-added (commercial) product on order. ( http://www.alos-restec.jp/en / )

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