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Oleksiy Kravchenko
Senior Data Scientist, Zoral Labs, Kyiv
Satellite orbits
Geostationary satellite images - MSG
Polar orbiting images - MODIS
Multispectral data
 Data
– Low resolution
• MODIS (250м)
• Proba-V (100м)
• Sentinel-3 (300 m)
– Medium resolution
• Land...
Applications
 Agriculture
– Agricultural statistics
• Crop area estimation
– Yield forecasting
– Crop state & dynamics mo...
7
Satellite features for crop classification
L5 2011-11-08
EO1 2012-05-23
L7 2012-05-10
Sich2 2012-04-26
Temporal dynamics
Crop rotation violations (3 years period)
Rapeseed area
2011,2013: 490 ha
2011,2012: 430 ha
2012,2013: 435 ha
2013: 1540 h...
Lviv site, SAR data example, 2013
Landsat8
2013-06-15 Radarsat-2
2013-06-13
 No clouds problems
 Better crop
separation ...
Gorodotsky county, Lviv region, Ukraine
Crop mapping, 2013
• Classification accuracy
– total 81 %
– Winter rapeseed: 96%
–...
Crop area estimation
Project ЕС JRC “Crop area
estimation with satellite images
in Ukraine”, 2009-2011
Satellite data Grou...
Area estimation
Odesa region
   regsample yVyV

rel_eff
  x yybxreg x 
 Vy
Nn
Nnn
G
n
reg
x
y()



...
Yield forecast
Winter wheat yield forecast, 2013
Forecast issue date – May 1st , 2013
Precision agriculture
Irrigation monitoring
August, 2012
Vegetation State Estimation (Forward &
Inverse Problems)
Leaf model
(PROSPECT)
Canopy RTM
(4SAIL)
Atmosphere RTM
(6S)
ll T...
Satellite product validation (crop state)
• Ukrainian GEO JECAM polygon
• Hemispherical photography (circular fisheye lens...
Illegal cropping
znaydeno.com.ua
Illegal crops on 5.9 Ha, 7k hryvnas fines payed
Satellite image preprocessing.
Sentinel-2 example
Source: SPOT SATELLITE
GEOMETRY HANDBOOK, 2002
Direct model
Model parameters
• Time, location, velocity (satellite center mass), 1Hz
• Exterior attitude, 1Hz
– measured by star track...
Inverse model (RPC)
Direct model georeferensing of Sich2
satellite
• Geolocation error
– RapidEye (20-50 м)
– Landsat5 (30 м)
– Spot6,7 (10 м)...
Drones georeferencing by telemetry
data only
Processing pipeline
Image based registration methods
Linear correlation surface Phase correlation
   
   
 
*
1 2
*
1 2
, ,
, ,
d dx...
Phase correlation explained
Accuracy estimation
Linear vs. phase correlation
10-100 more control points
Odesa region
20 м ~ 6”
Sich2 image georeferencing using
Landsat data as base map
400 m
0 m
Crimea, 2012-05-19
Orthorectification
1A
1А-Orho
Zaporizhzhya, 2012-02-11
Georef. example (1)
Khersonsky region, 2012 -05-12
1A
1A-Ortho
Georef. example (2)
Pixel assembly calibration
Corrections.
Metadata
2011-10-03
Trend analysis Corrections
provided in metadata
2012-09-
05
After
launch
After
launch
1 year
on orbit
Pixel assembly coreg...
Band coregistration
PAN NIR
Along-track
shifts
DEM
SRTM
Jitter: Sich2 example
Roll oscillation
Amplitude ~ 4m
DI
Pitch oscillation
Amplitude ~ 2m
DI
DI, pixels
Jitter (2)
2011-10-05 2011-10-28
Reducing jitter
 SSTL, 2008
 Attitude Determination through
Registration of Earth Observational
Imagery
 Momentum Wheel...
Pixel assembly coregistration (1)
Sentinel-2
Pleiades
Pixel assembly coregistration (2)
Satellite image vs. aerial
registration
airplane
tracks
Distortions due to tall buildings
Questions?
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков  Алексей Кравченко
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DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков Алексей Кравченко

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DataScience Lab, 13 мая 2017
Коррекция геометрических искажений оптических спутниковых снимков

Алексей Кравченко (Senior Data Scientist at Zoral Labs)
Мы рассмотрим разнообразие существующих спутниковых данных и способов их применения в сельском и лесном хозяйстве, картографировании земной поверхности. Далее сфокусируемся на задаче геометрической коррекции снимков как первом шаге процесса обработки спутниковых данных, включая геопривязку снимков, регистрацию изображений, субпиксельную идентификацию контрольных точек, совмещение каналов. Также расскажем о некоторых интересных и неожиданных подходах к определению ориентации и jitter спутников и построению маски облачности.
Все материалы: http://datascience.in.ua/report2017

Veröffentlicht in: Technologie
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DataScience Lab2017_Коррекция геометрических искажений оптических спутниковых снимков Алексей Кравченко

  1. 1. Oleksiy Kravchenko Senior Data Scientist, Zoral Labs, Kyiv
  2. 2. Satellite orbits
  3. 3. Geostationary satellite images - MSG
  4. 4. Polar orbiting images - MODIS
  5. 5. Multispectral data  Data – Low resolution • MODIS (250м) • Proba-V (100м) • Sentinel-3 (300 m) – Medium resolution • Landsat-7 (30м) • Landsat-8 (30м) • Sentinel-1 (20м, 2013) • Sentinel-2 (10м, 2015) Довжина хвилі, нм Відбиваючаздатність Поглинання хлорофілу Поглинання води Поглинання сухої біомаси Визначається внутрішньою структурою покриву Видимий (VIS) Ближній інфрачервоний (NIR) Середній інфрачервоний (SWIR) NDVI = (NIR – RED)/ (NIR + RED)
  6. 6. Applications  Agriculture – Agricultural statistics • Crop area estimation – Yield forecasting – Crop state & dynamics monitoring – Drought monitoring – Irrigated land monitoring – Crop rotation control, subsides control – Precision agriculture • Variable prescription maps  Forestry – Clear-cuts mapping, species mapping – Biomass estimation  Land cover mapping
  7. 7. 7 Satellite features for crop classification L5 2011-11-08 EO1 2012-05-23 L7 2012-05-10 Sich2 2012-04-26
  8. 8. Temporal dynamics
  9. 9. Crop rotation violations (3 years period) Rapeseed area 2011,2013: 490 ha 2011,2012: 430 ha 2012,2013: 435 ha 2013: 1540 ha 2012 2220 ha 2011 1610 ha
  10. 10. Lviv site, SAR data example, 2013 Landsat8 2013-06-15 Radarsat-2 2013-06-13  No clouds problems  Better crop separation with SAR than with optical data Winter rapeseed Maize
  11. 11. Gorodotsky county, Lviv region, Ukraine Crop mapping, 2013 • Classification accuracy – total 81 % – Winter rapeseed: 96% – +10% by using SAR data
  12. 12. Crop area estimation Project ЕС JRC “Crop area estimation with satellite images in Ukraine”, 2009-2011 Satellite data Ground data Processing · Orthorectification · Segmentation · Classification Stratified Area Frame Sampling Along the road survey % pixels classified as cereals %oatsingroundsurvey SegmentsCrop field boundariesLC map Area estimates (pixel counting) Data fusion Adjustment of area estimates (Regression estimator) Final results · Area estimates · Accuracy assessment Data: MODIS AWiFS Landsat-5/TM LISS-III RapidEye Satellite data effect: costs decrease in 1.5 times
  13. 13. Area estimation Odesa region    regsample yVyV  rel_eff   x yybxreg x   Vy Nn Nnn G n reg x y()            1 1 3 2 1 2 2 2 2  G k x x x  3 3   ( )Vy nys rreg xy  1 12 2    regsample yVyV ˆˆrel_eff  r = 0.986 rel_eff = 33.4 r = 0.997 rel_eff = 165.8 Data Area th. ha Error (2σ) ths. Ha % Sample 108.32 51.2 57.4% MODIS 95.18 8.88 9.32% Landsat 96.18 3.98 4.14% Ministry of Agric. 101,0 - -
  14. 14. Yield forecast Winter wheat yield forecast, 2013 Forecast issue date – May 1st , 2013
  15. 15. Precision agriculture
  16. 16. Irrigation monitoring August, 2012
  17. 17. Vegetation State Estimation (Forward & Inverse Problems) Leaf model (PROSPECT) Canopy RTM (4SAIL) Atmosphere RTM (6S) ll TR , 3. Vegetation state estimation using radiometrically corrected satellite data Inverse problems 2. Vegetation state estimation using satellite data with atmospheric correction 1. Vegetation state estimation using in-situ spectra  ,, vs soilR lSLADLAI ,, mwab CCCN, ,, canopyR TOAR ' s s s s v v s dE k E dz dE a E E s E dz dE a E E s E dz dE K E E E w E dz                                     1 ( ) ( ) ( )i i ek K C k N     90 1,90 , 90 1,901 N N N R R R            
  18. 18. Satellite product validation (crop state) • Ukrainian GEO JECAM polygon • Hemispherical photography (circular fisheye lenses) LAI=0.22 fCover=16% ALA=16º LAI=4.0 fCover=79% ALA=65º
  19. 19. Illegal cropping znaydeno.com.ua Illegal crops on 5.9 Ha, 7k hryvnas fines payed
  20. 20. Satellite image preprocessing. Sentinel-2 example
  21. 21. Source: SPOT SATELLITE GEOMETRY HANDBOOK, 2002 Direct model
  22. 22. Model parameters • Time, location, velocity (satellite center mass), 1Hz • Exterior attitude, 1Hz – measured by star trackers • Interior camera attitude within satellite coordinate system – rotation matrix to align camera and star tracker • Angular position of each pixel in camera coordinates – Angular position of pixel assembly in focal plane – Lenses distortion
  23. 23. Inverse model (RPC)
  24. 24. Direct model georeferensing of Sich2 satellite • Geolocation error – RapidEye (20-50 м) – Landsat5 (30 м) – Spot6,7 (10 м) – Sich2 (200-700 м) • Kyiv (240 м) • Shatsk (550 м) Shatsky National Park Sich2, 2011-11-02 550m USGS approach for Landsat5
  25. 25. Drones georeferencing by telemetry data only
  26. 26. Processing pipeline
  27. 27. Image based registration methods Linear correlation surface Phase correlation           * 1 2 * 1 2 , , , , d dx y x y x x y y x y x y IM IM e IM IM           
  28. 28. Phase correlation explained
  29. 29. Accuracy estimation
  30. 30. Linear vs. phase correlation 10-100 more control points
  31. 31. Odesa region 20 м ~ 6” Sich2 image georeferencing using Landsat data as base map
  32. 32. 400 m 0 m Crimea, 2012-05-19 Orthorectification
  33. 33. 1A 1А-Orho Zaporizhzhya, 2012-02-11 Georef. example (1)
  34. 34. Khersonsky region, 2012 -05-12 1A 1A-Ortho Georef. example (2)
  35. 35. Pixel assembly calibration Corrections. Metadata 2011-10-03
  36. 36. Trend analysis Corrections provided in metadata 2012-09- 05 After launch After launch 1 year on orbit Pixel assembly coregistration (1) 1 year on orbit
  37. 37. Band coregistration PAN NIR Along-track shifts DEM SRTM
  38. 38. Jitter: Sich2 example Roll oscillation Amplitude ~ 4m DI Pitch oscillation Amplitude ~ 2m DI DI, pixels
  39. 39. Jitter (2) 2011-10-05 2011-10-28
  40. 40. Reducing jitter  SSTL, 2008  Attitude Determination through Registration of Earth Observational Imagery  Momentum Wheel Activation (Y-Axis) - DU000373
  41. 41. Pixel assembly coregistration (1) Sentinel-2 Pleiades
  42. 42. Pixel assembly coregistration (2)
  43. 43. Satellite image vs. aerial registration airplane tracks Distortions due to tall buildings
  44. 44. Questions?

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