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Eng remote sensing and image measurement
Remote sensing uses sensors on platforms like satellites or aircraft to collect imagery and geospatial data of the Earth. Various sensors can extract different types of information like color, geometry, or 3D coordinates using principles like stereo imagery. Mathematical models like collinearity equations relate image coordinates to ground coordinates. Sensor position and attitude can be estimated using ground control points. 3D measurements are possible with stereo imagery. Different sensor types exist for applications like vegetation monitoring, land use mapping, and disaster monitoring.
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Eng remote sensing and image measurement
- 1. Remote Sensing; Geospatial DataAcquisition from Imagery
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- 3. What kinds ofinformation can we extract from imagery data? In case of Camera Color Directional Vector Or Geometric information.
- 4. Principle of geometricmeasurement from Imagery Position and Attitude of Camera when taking a picture or an image Vector of light or ray from an object （3D directional vector）
- 5. Principle of 3Dmeasurement using Stereo Imagery 3D coordinates of an object can be determined as an intersection point of two light rays.
- 6. More robust andaccurate measurement from a series of images.
- 7. Mathematical formulation i )co-linearity equation CRS: Coordinate System O: Center of Projection (Focus p.) (X0,Y0,Z0) : Ground CRS z κ Sensor CRS （x,y,z） φ Rotating angle of this coor.sys. (ω,φ,κ) : 3 axis attitude y x ω （地上座標系からみたセンサ座標系の回転角（傾き）） Image plane (Film plane) parallel to xy plane of sensor CRS f: focal length Z X Ai (Xa,Ya,-f): Image point of A Based on sensor CRS Ray A Y (X1, Y1,Z1) Ground CRS Co-linearity Equation（共線条件式） O, Ai, A are on the same ray (straight line) in 3D space
- 8. Co-linearity Equation（共線条件式） a11(X1-X0)+a21(Y1-Y0)+a31(Z1-Z0) X =f a13(X1-X0)+a23(Y1-Y0)+a33(Z1-Z0) Y = f a12(X1-X0)+a22(Y1-Y0)+a32(Z1-Z0) a13(X1-X0)+a23(Y1-Y0)+a33(Z1-Z0) Image coordinate of a target Ground coordinate of a target Sensor position aij = aij(ω,φ,κ）: Rotation Matrix Sensor attitude
- 9. Estimation of sensorposition and attitude using GCP (External orientation) Position and attitude of sensor cood.sys. (X0, Y0,Z0) : Position (ω,φ,κ): Attitude six unknown parameters GCP’s image coordinates Ai(xa, ya, -f) A(X1,Y1,Z1) Given : f (focal length) GCP: Ground Control Point Collinearity Eq. .............. xa = f .............. .............. ya = f .............. Non-linear least squares method Estimated ^ ^ ^ (X0, Y0, Z0) ^ ^ (ω,φ,κ) ^
- 10. 3D measurement withstereo images Imaging plane Image coordinates have to be measured Image or sensor with given or estimated position/attitude (x,y,z) Image or sensor with given or estimated position/attitude
- 11. Basic Concept for3D Building Extraction r G r o u n o o f d 3D information is key to differentiate the roofs from the objects on the ground
- 12. TLS （Three LineScanner）; Example of Digital Camera for 3D Mapping Stabilizer データ処理 装置 Gyro 3 line CCD array ＧＰＳ 位置デー タ 進行 方向 画像表示 装置 データ記録 装置 後方 鉛直 前方 ■Specifications ・Resolution 10cm(x-y)、20cm(z) ・continuous strip of digital imagery ・B/W and color imaging
- 13. Imaging mode ofTLS ■Stereo (triplet) images can be acquired simultaneously Aft image Nadir image Fore image
- 14. Images taken fromdifferent angles • It can acquire the images from three different view point. • No distortion of altitude comparison in flight direction. Forward 2014/2/17 Nadir Backward 14
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- 16. Laser Scanner orProfiler
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- 18. Urban Terrain withLaser Scanner 国土交通省国土地理院提供
- 19. Microwave Sensors a) RealAperture Radar return signal intensity range direction azimuth direction pulse length return time
- 21. To improve resolutionof cross-track(range) direction in processing return signal
- 22. b) Synthetic ApertureRadar - Applying pulse compression for along track (azimuth) direction Ground resolution : 1m~ Improving ground resolution by using Doppler effect
- 29. b) Change infrequency of return signal due to Doppler effect c) Characteristic of matched filter d) Output from matched-filter for receiving point target A
- 30. Geometry of RadarImage sensor incident wave angle of incidence aspect angle surface direction of flight angle of incidence off-nadir angle azimuth direction Distortions of Radar Imagery B' A' AB range direction
- 33. 航 空 機搭 載 SAR画 像 の 例
- 34. Principles of RemoteSensing Acquiring information of objects through electromagnetic wave reflected or radiated by the objects Platform Sun Sensor Atmosphere Radiation Spectral reflection Strengths: object Simultaneous observation of wide areas Homogeneous data Digital data Limitations, problems: Reference data is required for quantitative measurement Only information reflected in electromagnetic wave can be observed. (Only "visible" objects!)
- 35. Example of RemoteSensing Satellite
- 36. ALOS: Advanced LandObservation Satellite
- 37. Payloads PRISM (Panchromatic Remote-sensing Instrument forStereo Mapping) AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) PALSAR (Phased Array type Lband Synthetic Aperture Radar) DRC antenna Solar battery pane
- 38. PRISM Panchromatic Remote-sensing Instrumentfor Stereo Mapping Triplet observation for stable generation of DEM with 3-5m elevation error Specification fore aft nadir
- 39. AVNIR-2 Advanced Visible andNear Infrared Radiometer type-2 Specification
- 40. PALSAR Phased Array typeL-band Synthetic Aperture Radar Specification
- 41. Spectral reflectance ofvegetation, soil and water: (By measuring reflectance of each spectrum, objects can be identified.)
- 42. Spectral reflectance oftree species: (By measuring reflectance of each spectrum, objects can be identified.)
- 43. Spectral reflectance ofrocks and minerals: (By measuring reflectance of each spectrum, objects can be identified.)
- 44. Physical features thatcould be measured with electromagnetic wave Wave length U.V. Visible I.R. 0.1 micro meter (100nm) 1.0 micro meter Ozone hole Land cover/use Vegetation (primary production) 10.0 micro meter 100. micro meter Ground surface temperature Sea surface temperature 1mm Microwave 1cm 10cm 100cm Precipitation Sea surface wind (direction, velocity) Wave height, direction Snow depth Soil water content vegetation biomass (standing biomass)
- 45. Characteristic of atmosphericspectral transmittance
- 46. For Active MicrowaveSensors
- 47. Biomass Estimation by MicrowaveScatterometer Stronger back scattering (surface + volume scattering) Weaker
- 48. Measurement model; howto associate sensor data with physical properties data The other environmental model affecting Environmental model in a broader sense Sensor model (sensitivity) Atmospheric model Active sensor affecting Sun （Passive sensor） affecting Platform model （fluctuation in position/attitude) affecting Electromagnetic wave model (propagation, absorption, scattering…) affecting Object model Radiation/reflection Shape/geometry Seasonal change/movement etc. Estimating “truth” with limited observation data with MLE or Maximum Likelihood Estimation. （最尤推定）
- 49. Examples of RemoteSensors
- 50. 1) Sensor Typesfor Remote Sensing Sensors Passive Non Scanning Type....Cameras Scanning Type... (Scanners) Active Non Scanning Type.. Scanning Type... E X A M P L E S - Photogrametric CCD Image Sensors camera Multispectral Scanners - Multispectral Microwave Radiometer camera -Sea surface temperature, Vapor content, Salt content of water etc. - Total Station (Range Measurement) - LIDAR - Microwave altimeter - Geoid, Sea surface height etc. Microwave scatterometer - Velocity and direction of sea surface wind - Intensity of rainfall - Water content of soil etc. Imaging radar - Synthetic Aperture Radar - Side Looking Radar (Real Aperture Radar) Laser Range Imager
- 51. Optical Sensors Multi-spectral scanners(MSS) mechanicalscanner An Example of Classical Scanner
- 52. detector spectroscope scan mirror folding mirror Flightdirection （v） instantaneous field of view
- 53. Linear Array Sensor (LinearCCD) Flight direction Optics Scan Line Schematic diagram of data acquisition by push broom scanner
- 54. Band 1 Band 2 Band3 Concept of Bands
- 55. NOAA AVHRR Datareceived at AIT -Data receiving started from Oct. 1997. -Improvement of Processing software is on-going (by Aug.). -geometric correction (extending GCP files to SE Asia) -atmospheric correction -Processed data delivery may start from Sept.(personal anticipation)
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- 60. NDVI Seasonal Changes Red:High NDVI values Yellow: Low NDVI values
- 62. trees Hyper-spectral Sensors Yasuda Hall Gotenshitafield 6000 Vegetation Asphalt 5000 Athletic Field Hall 4000 Pond 3000 2000 1000 0 400 Sanshiro pond 500 blue 600 green 700 800 900 1000 Near Infrared Asphalt (Hongo street) （東京大学生産技術研究所 安岡研究室提供） （単位：nm（ナノメータ））
- 63. Ground/Sea Surface Temperaturemeasured by the radiation in far infrared wave length (1999/3/1, 21:00pm)
- 64. 22. 0 y = 0. 0839x+ 9. 7174 R ²= 0. 812 ace S urf Tem p. 1℃） （ 20. 0 18. 0 16. 0 補正後平均値 14. 0 線形 (補正後平均値) 12. 0 10. 0 20. 0 30. 0 40. 0 50. 0 60. 0 70. 0 80. 0 A i Tem p. 0. r （ 1℃） R el onshi betw een S urf Tem p and A i Tem p ati p ace r 90. 0 100. 0 110. 0 120. 0
- 65. Microwave Scatterometer -Active MicrowaveSensor -By emitting microwave to an object, information can be extracted from scattered or return microwave
- 66. Basic idea underlyingSurface Wind Measurement using Microwave scatterometer Weak Scattering (Reflectance) Strong Scattering (Reflectance) Surface Wind Sea Surface Surface Wind Observation (Emission of Microwaves)
- 69. Wind Velocity 2m/s(rms) :3-20m/s 10% : 20-30m/s Wind Direction 20deg.(rms): 3-30m/s Spatial Resolution 25km : 0deg. Cell 50km : Wind Cells Location Accuracy 25km(rms) : Absolute 10km(rms) : Relative Coverage Mass Power Data Rate 90% of ocean every 2days 300kg 275W 2.9kbps
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- 71. Biomass Estimation by MicrowaveScatterometer Stronger back scattering (surface + volume scattering) Weaker
- 76. Microwave Radiometer - PassiveMicrowave Sensor Measuring radiated microwave from an object AMSR-E Instrument Description http://www.ghcc.msfc.nasa.gov/AMSR/html/amsr_products.html The PM-1 AMSR is a twelve channel, six frequency total power passive microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz
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- 83. AMSR-E Level 2EOS Standard Data Products PARAMETER ACCURACY SPATIAL RESOLUTION Brightness Temperature 0.2 - 0.7 K 6 - 76 km Ocean Wind Speed 1.5 m/s 12 km Water Vapor Over Ocean 0.2 g/cm2 23 km 3 mg/cm2 23 km 0.5 K 76 km Surface Soil Moisture 0.06 g/cm3 where vegetation is less than 1.5 kg/m2 25 km (Equal Area Earth Grid) Global Rainfall Ocean: 1 mm/hr or 20% (whichever is greater) 10 km Global Rain Type (Convection fraction) Land: 2 mm/hr or 40% (whichever is greater) N/A 10 km Cloud Liquid Water Over Ocean Sea Surface Temperature
- 84. Satellite Missions For Details: https://directory.eoportal.org/web/eoportal/sat ellite-missions(English) http://www.restec.or.jp/knowledge/satellite_ter m.html (Japanese)
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- 88. Landsat 1 to8 Satellite Launch Date Landsat 1 Period of Operation 23 July 1972 Landsat 6 Decommissioned 6 January 1978 Decommissioned 25 22 January 1975 February 1982 Decommissioned 31 5 March 1978 March 1983 Decommissioned 16 July 1982 June 2001 Thematic Mapper stopped acquiring 1 March 1984 data 18 November 2011 October 1993 Failed on Launch Landsat 7 15 April 1999 Operating in SLC-Off Mode after May 2003 Landsat 8 February 2013 Due to be launched February 2013 Landsat 2 Landsat 3 Landsat 4 Landsat 5 (1972 – present) http://www.ga.gov.au/ausgeonews/ausgeonews2012 09/landsat.jsp
- 89. Landsat TM Image(spatial resolution: 30m)
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- 92. SPOT-5 sample imageof Naples (Italy) in 2002 (image credit: CNES) the spatial resolution of the imagery to < 3 m in the panchromatic band and to 10 m in the multispectral mode https://directory.eoportal.org/web/eoportal/satellite-missions/s/spot-5
- 93. MOS-1 Main Characteristics ofthe MOS-1 ------------------------------------------Scape : Box type with expanding type solar cell paddle (one wing) Bus unit 1.26mx2.4mx1.48m Solar cell paddle, total length 5.28mx2m Weight : Approx. 740kg Attitude control : Three axes control Design life : 2 years ------------------------------------------Launch vehicle : H-I Launch site : Tanegashima Space Center, Kagoshima Launch date : February 7, 1990 ------------------------------------------Orbit Type : Sun synchronous subrecurrent orbit Altitude : Approx. 909km Inclination : Approx. 99deg. Period : Approx. 103min.
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- 95. Optical System (OPS) Band 1 3 4* .55- .60 Frequency (µm) 2 .63 - .69 .76 - .86 .76 - .86 GSD (M) 18.3 x 24.2 Scene size (km) 75 x 75 Revisit interval (days) 44 at equator * Viewing 15.3° forward, provides stereoscopic capability when used with band 3 Synthetic Aperture Radar (SAR) Spe ctral Ban ds Frequ ency Polaris ation Incidence Angle Spatial Resolution Swath (Km) LBa nd 1.275 GHz HH 35.21° off nadir 18 m 75
- 97. -Commercial satellite -Launched byCanada -Only SAR (C band) -fine resolution. mode - scan SAR mode
- 99. ADEOS Sensors in ADEOS 1.OCTS - Ocean Color and Temperature Scanner 2. AVNIR - Advanced Visible and Near Inrared Radiometer 3. NSCAT - NASA Scatterometer 4. TOMS - Total Ozone Mapping Spectrometer 5. IMG - Interferometric Monitor for Greenhouse Gases 6. POLDER - Polarization and Directionality of the Earthe's Reflectance 7. ILAS - Improved Limb Atmospheric Scatterometer
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- 108. ALOS(Advanced Land ObservationSatellite)
- 109. flight direction Fore Aft Nadir flight direction Fore ForeNadir Aft Nadir Aft
- 112. ASTER G-DEM Internationaljoint project between METI and NASA Earth observing sensor developed by Japan (METI) flying on Terra Launched in December 1999, in stable operation for more than 7 years ASTER provides: Flight direction Backward satellite Terra Nadir 1) Surface condition The earth surface is observed in visible to thermal infrared (invisible to human eyes) spectral regions to obtain detailed information on the condition and distribution of the surface (vegetation, geology, etc.). 2) Surface temperature The distribution of surface temperature is observed by the thermal infrared sensor to study the urban heat island effect and other phenomenon in detail. 3) DEM DEM is derived from a stereo-pair of images over a single area acquired in nadir and backward viewing angles.
- 113. Features of ASTERG-DEM Joint project between METI and NASA Generation of global land DEM based on the ASTER coverage Enhanced accuracy due to the use of multiple ASTER data over one region User friendly with the capability for selective cropping ASTER scene (60km x 60km) Generation of seamless DEM using all ASTER data ever acquired over the target area Automated processing A seamless wide-coverage Red-colored area: ASTER coverage (1.1 million scenes) Deeper red indicates more frequent observations, thus providing higher accuracy. ASTER G-DEM DEM applied to all land area Easy to use, allowing for selective cropping
- 114. Comparison with otherDEMs ASTER G-DEM SRTM3 GTOPO30 Shuttle Radar Topography Mission Data at 3 Arc-Seconds Global 30 Arc-Second Elevation Data Set Data source ASTER Space shuttle radar From organizations around the world that have DEM data Generation and distribution METI of Japan / NASA NASA/NGA/USGS USGS Release year 2009 ～ (planned) 2003 ～ 1996 ～ Data acquisition period 2000 ～ ongoing 11 days （in 2000） DEM resolution 30m 90m 1000m DEM accuracy (stdev.) ±7m ±10m ±30m DEM coverage 83 degrees north ～83 degrees south 60 degrees north ～56 degrees south Global Area of missing data Areas with no ASTER data due to constant cloud cover Topographically steep area None (due to radar characteristics) NGA：National Geospatial-intelligence Agency USGS：United States Geological Survey The ASTER G-DEM is the only sophisticated global coverage DEM, which will be widely used as the global standard.