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# Eng remote sensing and image measurement

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### Eng remote sensing and image measurement

1. 1. Remote Sensing; Geospatial Data Acquisition from Imagery
2. 2. Digital Imaging Sensors!
3. 3. What kinds of information can we extract from imagery data?  In case of Camera Color Directional Vector Or Geometric information.
4. 4. Principle of geometric measurement 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. 5. Principle of 3D measurement using Stereo Imagery 3D coordinates of an object can be determined as an intersection point of two light rays.
6. 6. More robust and accurate measurement from a series of images.
7. 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. 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. 9. Estimation of sensor position 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. 10. 3D measurement with stereo 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. 11. Basic Concept for 3D 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. 12. TLS （Three Line Scanner）; 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. 13. Imaging mode of TLS ■Stereo (triplet) images can be acquired simultaneously Aft image Nadir image Fore image
14. 14. Images taken from different 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
15. 15. 3方向画像から作成した3次元モデル 3次元データを 使った変化の 自動検出例
16. 16. Laser Scanner or Profiler
17. 17. Electric Wire Electric Tower Tree tops
18. 18. Urban Terrain with Laser Scanner 国土交通省国土地理院提供
19. 19. Microwave Sensors a) Real Aperture Radar return signal intensity range direction azimuth direction pulse length return time
20. 20. To improve resolution of cross-track(range) direction in processing return signal
21. 21. b) Synthetic Aperture Radar - Applying pulse compression for along track (azimuth) direction Ground resolution : 1m~ Improving ground resolution by using Doppler effect
22. 22. b) Change in frequency of return signal due to Doppler effect c) Characteristic of matched filter d) Output from matched-filter for receiving point target A
23. 23. Geometry of Radar Image 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
24. 24. 航 空 機 搭 載 SAR画 像 の 例
25. 25. Principles of Remote Sensing 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!)
26. 26. Example of Remote Sensing Satellite
27. 27. ALOS: Advanced Land Observation Satellite
28. 28. Payloads   PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) PALSAR (Phased Array type Lband Synthetic Aperture Radar) DRC antenna Solar battery pane
29. 29. PRISM Panchromatic Remote-sensing Instrument for Stereo Mapping Triplet observation for stable generation of DEM with 3-5m elevation error Specification fore aft nadir
31. 31. PALSAR Phased Array type L-band Synthetic Aperture Radar Specification
32. 32. Spectral reflectance of vegetation, soil and water: (By measuring reflectance of each spectrum, objects can be identified.)
33. 33. Spectral reflectance of tree species: (By measuring reflectance of each spectrum, objects can be identified.)
34. 34. Spectral reflectance of rocks and minerals: (By measuring reflectance of each spectrum, objects can be identified.)
35. 35. Physical features that could 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)
36. 36. Characteristic of atmospheric spectral transmittance
37. 37. For Active Microwave Sensors
38. 38. Biomass Estimation by Microwave Scatterometer Stronger back scattering (surface + volume scattering) Weaker
39. 39. Measurement model; how to 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）
40. 40. Examples of Remote Sensors
41. 41. 1) Sensor Types for 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
42. 42. Optical Sensors Multi-spectral scanners(MSS) mechanical scanner An Example of Classical Scanner
43. 43. detector spectroscope scan mirror folding mirror Flight direction （v） instantaneous field of view
44. 44. Linear Array Sensor (Linear CCD) Flight direction Optics Scan Line Schematic diagram of data acquisition by push broom scanner
45. 45. Band 1 Band 2 Band 3 Concept of Bands
46. 46. NOAA AVHRR Data received 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)
47. 47. 1997.Jan.
48. 48. 1997.Apr.
49. 49. 1997.Jul.
50. 50. 1997.Oct.
51. 51. NDVI Seasonal Changes Red: High NDVI values Yellow: Low NDVI values
52. 52. trees Hyper-spectral Sensors Yasuda Hall Gotenshita field 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））
53. 53. Ground/Sea Surface Temperature measured by the radiation in far infrared wave length (1999/3/1, 21:00pm)
54. 54. 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
55. 55. Microwave Scatterometer -Active Microwave Sensor -By emitting microwave to an object, information can be extracted from scattered or return microwave
56. 56. Basic idea underlying Surface Wind Measurement using Microwave scatterometer Weak Scattering (Reflectance) Strong Scattering (Reflectance) Surface Wind Sea Surface Surface Wind Observation (Emission of Microwaves)
57. 57. 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
58. 58. NSCAT_ant_imsk http://www.ee.byu.edu/ee/mers/NSCAT-1.html
59. 59. Biomass Estimation by Microwave Scatterometer Stronger back scattering (surface + volume scattering) Weaker
60. 60. Microwave Radiometer - Passive Microwave 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
61. 61. http://www.eoc.nasda.go.jp/guide/satellite/sendata/tmi_e.html
62. 62. AMSR-E Level 2 EOS 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
63. 63. 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)
67. 67. Landsat 1 to 8 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
68. 68. Landsat TM Image (spatial resolution: 30m)
69. 69. SPOT 1 to 6
70. 70. SPOT-5 sample image of 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
71. 71. MOS-1 Main Characteristics of the 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.
72. 72. JERS-1
73. 73. 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
74. 74. -Commercial satellite -Launched by Canada -Only SAR (C band) -fine resolution. mode - scan SAR mode
75. 75. 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
76. 76. TRMM
77. 77. EOS-AM and PM TERRA (EOS-AM) http://terra.nasa.gov/ http://aqua.nasa.gov AQUA (EOS-PM)
78. 78. ENVISAT MERIS ASAR AATSR RA-2 MWR DORIS GOMOS MIPAS SCIAMACHY LRR http://envisat.esa.int/
79. 79. High Resolution Satellites 内閣官房・宇宙戦略本部事務局作成 http://www.kantei.go.jp/jp/singi/utyuu/RSSkentou/dai1/siryou2.pdf
80. 80. Geo-Eye http://news.satimagingcorp.com/2008/09/geoeye1_satellite_sensor_launched_successfully_from_vande nberg_air_force_base_in_california_.html http://www.spaceimaging.co.jp
81. 81. ALOS(Advanced Land Observation Satellite)