DigitalGlobe Overview

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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DigitalGlobe Overview

  1. 1. DigitalGlobe Overview December 2013 Kumar Navulur, PhD 1
  2. 2. DigitalGlobe Constellation Quickbird GeoEye-1 WorldView-1 WorldView-2 IKONOS 0.82 m GSD 150,000 km2 /day WorldView-3 0.65 m GSD 200,000 km2 /day 0.41 m GSD 700,000 km2 /day 0.5 m GSD 1,500,000 km2 /day 0.46 m GSD 1,200,000 km2 /day 2014 0.31 m GSD 680,000 km2 /day DigitalGlobe Proprietary and Business Confidential
  3. 3. DigitalGlobe Archive 4 2 0 0 0 0 0 0 0 0 Sq Km 3
  4. 4. WorldView2 & 3 VNIR and CAVIS VNIR bands are optimized for different missions 1.0 0.9 yellow 590-630 coastal 400-452 blue 448-510 green 514-586 red 632-692 red-edge 706-746 NIR1 772-890 NIR2 866-954 0.8 WorldView2/3 – VNIR Bands 0.7 0.6 LDCM Panchromatic LDCM 0.5 0.4 LDCM Blue LDCM Green LDCM Red LDCM NIR WorldView3 - CAVIS VNIR Bands 897-927 water-2 0.3 0.2 0.1 405-420 Desert Clouds 459-479 Blue Aerosol-1 525-585 green 635-685 Red Aerosol-2 845-885 water-1 930-965 water-3 0.0 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 Aeronet 10nm Bands 4
  5. 5. WorldView3 SWIR and CAVIS3 SWIR bands are optimized for different missions 1.0 WorldView3 – 8 SWIR Bands 0.9 0.8 1640-1680 MS11 2145-2185 2235-2285 MS13 MS15 0.7 0.6 1195-1225 MS9 0.5 0.4 1550-1590 MS10 LDCM Cirrus 1710-1750 MS12 2185-2225 MS14 2295-2365 MS16 LDCM SWIR 2 LDCM SWIR 1 WorldView3 - CAVIS3 SWIR Bands 2105-2245 Aerosol-3 0.3 0.2 0.1 1370-1410 CIRRUS 1620-1680 snow / cloud 1220-1252 Aerosol-NDVI 2105-2245 Aerosol-3 0.0 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 Aeronet 10nm Bands 25nm 5
  6. 6. Satellite Spectral Comparison MODIS (250+ m) ASTER 2 (R) (15/30 m) 4 (NIR) backward (1.2 / 3.7) Coastal CAVIS (30 m) 300 3 (NIR) nadir Red Blue Yellow Edge Green Red Aerosol 1 Desert Cloud 7 (SWIR 3) 9 (SWIR 5) Stereo 1 (G) WV-3 5 (SWIR 1) 6 (SWIR 2) NIR 2 SWIR 2 NIR 1 Aerosol 2 SWIR 1 Water 2 SWIR 4 SWIR 3 NDVI - SWIR Snow 10 (SWIR 6) 8 (SWIR4) SWIR 6 SWIR 5 SWIR 8 SWIR 7 Aerosol 3 Cloud Height Parallax Green 500 Wavelength in nanometers Water 1 700 Water 3 900 Cirrus Aerosol 3 1100 1300 1500 1700 1900 2100 2300 2500 DigitalGlobe Proprietary and Business Confidential 6
  7. 7. CAMP processor enables high quality information & insight extraction, and multi-temporal analysis-1 Color image of a typical 21km x 17km scene Addressed Effects • Opaque clouds • Cirrus clouds • Aerosols • Water vapor • Ice/Snow • Shadows • BRDF Core Products • Reflectance imagery accurate to within 1% absolute • Utility masks located to ~1m CE90/LE90 4 km visibility 11 km 7
  8. 8. CAMP processor enables high quality information & insight extraction, and multi-temporal analysis-2 Aerosol variability on a typical 21km x 17km scene Addressed Effects • Opaque clouds • Cirrus clouds • Aerosols • Water vapor • Ice/Snow • Shadows • BRDF Core Products • Reflectance imagery accurate to within 1% absolute • Utility masks located to ~1m CE90/LE90 4 km visibility 11 km 8
  9. 9. CAMP processor enables high quality information & insight extraction, and multi-temporal analysis-3 Atmospherically corrected color image of a typical 21km x 17km scene Addressed Effects • Opaque clouds • Cirrus clouds • Aerosols • Water vapor • Ice/Snow • Shadows • BRDF Core Products • Reflectance imagery accurate to within 1% absolute • Utility masks located to ~1m CE90/LE90 4 km visibility 11 km 9
  10. 10. Where are we headed: Enable Automated Information Extraction Water vapor Distortion Reflectance or Radiance Enables developing spectral models for automated information extraction Reflectance Aerosol Distortion Wavelength (nm) Blue Green Red Infrared DigitalGlobe Proprietary and Business Confidential 10
  11. 11. NDVI values from TOA and surface reflectance Water Vapor absorption 0.887 (~10%) 0.894 (~13%) 0.811 0.789 Scattering 11
  12. 12. NDVI from TOA reflectance Longmont (August 10, 2011 – WV2) NDVI from TOA reflectance no vegetation vigorous vegetation 12
  13. 13. NDVI from Surface Reflectance Longmont (August 10, 2011 – WV2) NDVI from surface reflectance LAI: 0.56 [m2 /m2] LAI: 4.53 [m2 /m2] LAI: 3.72 [m2 /m2] LAI: 4.45 [m2 /m2] no vegetation vigorous vegetation 13
  14. 14. Time-Series Data Set The data set used is composed of 21 images acquired between 2002 and 2009 by QuickBird over the city of Denver, Colorado. The time-series covers part of the downtown area and includes single family houses, skyscrapers, apartment complexes, industrial buildings, roads/highways, urban parks, and bodies of water. 14
  15. 15. Change Detection Denver – QB: July 17, 2002 Surface Refl. Change Detection DN Change Detection Denver – QB: August 22, 2008 Negative Change No-Change Positive Change 15
  16. 16. Time-series over Longmont, CO (2009-2012) Date meanSunAz meanSunEl meanSatAz meanSatEl 2010-06-05T18:02:26 141.0 68.9 165.1 71.8 2010-06-18T18:27:12 153.6 71.9 324.8 51.0 2010-06-24T18:09:52 141.9 70.0 218.4 80.9 2010-11-03T18:10:20 170.1 34.3 16.6 72.4 2010-11-03T18:10:30 170.2 34.3 18.0 78.0 2010-11-03T18:10:41 170.2 34.3 21.9 83.8 2010-11-03T18:10:52 170.3 34.3 93.3 89.0 2010-11-03T18:11:12 170.4 34.4 190.0 78.5 2011-03-29T17:59:02 153.4 50.6 77.3 62.6 2011-03-29T17:59:10 153.4 50.6 85.7 63.8 2011-03-29T17:59:21 153.5 50.6 98.1 64.7 2011-03-29T17:59:29 153.5 50.6 108.6 64.6 2011-03-29T17:59:40 153.6 50.6 120.8 63.5 2011-06-16T18:09:27 142.7 70.2 19.2 35.6 2011-08-09T18:33:50 161.6 64.9 248.9 57.6 2011-08-10T17:58:24 144.1 61.5 156.5 40.8 2011-08-10T17:58:15 144.1 61.5 153.6 43.0 2011-08-10T17:58:06 144.0 61.4 150.3 45.3 2011-08-20T18:30:05 162.5 61.5 258.4 66.5 2011-08-23T18:19:36 158.4 59.9 339.1 82.0 2011-08-23T18:20:10 158.6 59.9 214.7 77.4 2011-08-23T18:20:19 158.7 59.9 208.9 72.6 2011-09-22T18:18:55 166.8 49.6 0.9 77.6 2012-01-26T17:48:00 157.3 28.1 63.0 39.9 2012-04-13T18:18:10 159.9 58.0 115.4 87.0 2012-04-18T18:33:47 167.0 60.6 288.3 66.9 WV1 Image CatID 1 1020010009353000 2 10200100094A3700 3 10200100098B0A00 4 102001000A08B800 5 102001000A68D900 6 102001001109C500 7 10200100180FDD00 8 1020010018D32200 Date meanSunAz meanSunEl meanSatAz meanSatEl 2009-10-18T18:14:10 170.0 39.7 238.0 70.9 2009-10-18T18:14:05 169.9 39.7 250.5 73.9 2009-10-18T18:13:55 169.9 39.6 283.8 76.4 2009-10-18T18:13:44 169.8 39.6 316.9 73.8 2009-10-18T18:13:39 169.8 39.6 327.9 71.2 2011-03-29T18:30:23 165.6 52.6 281.2 49.2 2012-01-09T18:07:03 164.4 26.4 185.0 52.2 2012-01-09T18:06:09 164.1 26.3 64.9 83.4 345 70 0 WV2 QB WV1 Sun 15 330 30 60 315 50 40 45 300 60 30 20 zenith (°) WV2 Image CatID 1 1030010005098C00 2 103001000582F000 3 10300100051AFE00 4 103001000784E200 5 10300100079DC100 6 103001000760B200 7 10300100077FC700 8 103001000701FC00 9 103001000913F200 10 103001000A847800 11 103001000AA58D00 12 103001000A655E00 13 103001000A905200 14 103001000B335B00 15 103001000C1D2D00 16 103001000CC40500 17 103001000D763100 18 103001000CC55900 19 103001000D46A300 20 103001000D824500 21 103001000DB4AA00 22 103001000D272200 23 103001000D7FF200 24 10300100111AD800 25 10300100129FE800 26 1030010013B5B300 285 75 10 0 270 90 10 20 255 105 30 40 50 QB Image CatID 1 101001000A76CD00 2 101001000925E300 3 1010010009086100 Date meanSunAz meanSunEl meanSatAz meanSatEl 2009-10-18T17:50:58 162.7 38.6 66.1 70.7 2009-02-05T18:09:05 161.5 32.3 263.3 79.1 2009-01-08T17:59:34 162.5 25.9 87.1 69.1 240 120 225 135 60 70 210 150 195 180 165 16
  17. 17. WV2: DG-AComp accuracy 0.50 Field measurement DG-AComp RMSE (MS) concrete concrete 0.0070 asphalt 0.45 0.0127 0.40 Reflectance 0.35 0.30 0.25 0.20 0.15 RMSE (PAN) 0.10 concrete asphalt asphalt 0.05 0.0013 0.0018 0.00 C B G Y R RE N1 N2 PAN 17
  18. 18. Calibrated Tarps The tarps were manufactured to guarantee a flat spectral response between 400 nm and 1050 nm, with a peek-to-peek variation in reflectance less than 10% between 10° and 60° off-nadir [3][4]. [3] M. Pagnutti, K. Holekamp, R.E. Ryan, R.D. Vaughan, J.A. Russell, D. Prados, and T. Stanley, “Atmospheric correction of high spatial resolution commercial satellite imagery products using modis atmospheric products”, in Analysis of Multi-Temporal Remote Sensing Images, May 2005, pp. 115 – 119. [4] K. Holekamp, “NASA radiometric characterization”, in High spatial resolution commercial imagery workshop, Reston, VA, Nov. 2004 18
  19. 19. Data Set Location NASA Stennis Space Center, MS NASA Stennis Space Center, MS NASA Stennis Space Center, MS NASA Stennis Space Center, MS Brookings, SD Brookings, SD Brookings, SD Brookings, SD Wiggins, MS Wiggins, MS Wiggins, MS Park Falls, WI Date 7-Feb-06 10-Jan-04 14-Nov-02 12-Mar-05 15-Sep-03 7-Sep-02 20-Jul-02 18-Oct-05 15-Mar-06 7-Jan-06 25-Jan-06 5-Aug-05 Sat. Az. 208.2 259.7 274.2 268.4 284.4 191.4 349.7 298.2 319.5 300.0 296.6 261.5 Sat. El. 83.2 88.2 79.2 77.7 83.1 74.6 64.3 73.2 76.7 68.0 69.3 69.4 This set of 12 QuickBird images includes four 20 m2 spectrally-flat tarps having nominally 3.5, 22, 34, and 52% reflectance in the visible through NIR spectral region. 19
  20. 20. QB: DG-AComp accuracy (52, 34, 22, and 3.5% reflectance) 1.0 DG-AComp RMSE (MS) 52% 0.8 0.0155 34% 0.0127 22% 0.0155 3.5% 0.9 0.0082 Reflectance 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 B G R NIR 20
  21. 21. Information Extraction: Exploiting various dimensions of imagery Spectral Spatial/ Morphological Temporal DigitalGlobe Proprietary and Business Confidential 21
  22. 22. Information Extraction: Model Portability Istanbul – WV02: Feb. 19, 2010 Honolulu – WV02: Apr. 25, 2010 Rio de Janeiro – WV02: Jan. 19, 2010 New York – WV02: Dec. 18, 2009 Six classes of interest: 1. Grass 2. Tree 3. Water 4. Soil 5. Built-up 6. Shadow 22
  23. 23. Information Extraction: Model Portability Three different experiments using training pixels from: 1. Honolulu 2. Honolulu and Istanbul 3. Honolulu, Istanbul, and New York DN In all cases, the image of Rio de Janeiro has been used ONLY for validation. DN Kappa Coefficient Surf. Refl. Surf. Refl. 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Honolulu Honolulu, Istanbul Honolulu, Istanbul, NewYork 23

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