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Integration of sensors for photogrammetry and remote sensing

Integration of sensors for photogrammetry and remote sensing. 8 th semester, MS 2005. Overview on satellites and sensors operating in the optical spectrum. Earth observing system (EOS) Landsat S POT NOAA Other satellite programs Exercise: supervised classification of a Landsat TM image.

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Integration of sensors for photogrammetry and remote sensing

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  1. Integration of sensors for photogrammetry and remote sensing 8th semester, MS 2005

  2. Overview on satellites and sensors operating in the optical spectrum • Earth observing system (EOS) • Landsat • SPOT • NOAA • Other satellite programs • Exercise: supervised classification of a Landsat TM image

  3. NASA’s ESE • 1991: NASA started the Earth Science Enterprise (ESE), a program studying the Earth as an environmental system • ESE consists of: • Earth observing system (EOS) • Advanced processing network for processing, storing, and distributing data • Teams of scientists all over the world who will study the data

  4. Earth observing system (EOS) • consists of a series of satellites equipped with different sensors for long-term global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans

  5. EOS Terra • launched on December 18, 1999 • five onboard sensors • ASTER: Advanced Spaceborne Thermal Emission and Reflection Radiometer • CERES: Clouds and Earth's Radiant Energy System • MISR: Multi-angle Imaging Spectroradiometer • MODIS: Moderate-resolution Imaging Spectroradiometer • MOPITT: Measurements of Pollution in the Troposphere

  6. Landsat satellite program • program has been running since 1970’s • provides repetitive acquisition of high resolution multispectral data of the Earth's surface on a global basis • integral component of NASA's Earth Sciences Enterprise • 7 missions until 2005 • five different types of sensors: • Return Beam Vidicon (RBV) • Multispectral Scanner (MSS) • Thematic Mapper (TM) • Enhanced Thematic Mapper (ETM) • Enhanced Thematic Mapper Plus (ETM+)

  7. Landsat missions

  8. Landsat sensors

  9. Goals of Landsat 7 mission • provide timely, high quality visible and IR images of all landmass and near-coastal areas on the Earth • continually refreshing an existing Landsat database • data will be consistent with currently archived data in terms of acquisition geometry, calibration, coverage and spectral characteristics to allow comparison for global and regional change detection and characterization • support government, international and commercial communities • improved access to International Ground Station data

  10. Landsat 7 data distribution system

  11. Landsat 7 orbit • circular • Sun-synchronous (between 10:00 AM and 10:15 AM on the Equator) • near polar • repetitive (16-day Earth coverage cycle ) • nominal altitude of 705 km at the Equator • velocity 7.5 km/sec, each orbit takes nearly 99 min • just over 14 orbits per day

  12. Landsat 7 swath pattern

  13. ETM+ design • nadir-viewing, eight-band multispectral scanning radiometer • silicon detectors for bands 1-4 and 8 (panchromatic) are located in the the Primary Focal Plane • detectors for bands 5, 7, and 6 are located in the Cold Focal Plane • 32detectors for band 8, 16 detectors for bands 1-5 and 7, and 8 detectors for band 6

  14. Landsat 7 image acquisition • scenes placed the standard worldwide reference system • the WRS indexes orbits (paths) and scene centers (rows) into a global grid system comprising 233 paths by 248 rows • the ETM+ does not acquire data continually, acquisitions are scheduled in advanced using a Long Term Acquisition Plan (LTAP) • LTAP aspects : • seasonality of vegetation, niche-science communities • predicted vs. nominal cloud-cover • sun angle • missed opportunities for previous acquisitions • quality (cloud-cover) of previous acquisitions • scene clustering • system constraints (duty cycle, ground station locations, recorder capacity, etc.)

  15. Landsat 7 image products Program philosophy: to provide raw data

  16. Image Dimensions for a Landsat 7 0R Product BandNumber Resolution(meters) Samples(columns) Data Lines(rows) Bits perSample 1-5, 7 30 6600 6000 8 6 60 3300 3000 8 8 15 13200 12000 8 Landsat 7 0R product Size of the scene approx. 185 km x 180 km

  17. Applications of Landsat images • middle and small scale mapping • forest monitoring • mapping volcanic surface deposits • monitoring of natural disasters (floods, fires, slides)

  18. SPOT satellite program • SPOT = Système Pour l’Observation de la Terre • program started from an initiative of the French government in 1978, Sweden and Belgium joined before the launch of the first series of satellites • first system that employed pushbroom scanning techniques and off-nadir viewing (stereoscopic coverage)

  19. SPOT program – general features

  20. SPOT sensors SPOT 1, 2, 3 high resolution visible (HRV) imaging system SPOT 4 high resolution visible and infrared (HRVIR) imaging system SPOT 5 high resolution geometric (HRG) and high resolution stereoscopic (HRS) imaging system

  21. Acquisition of stereoimagesacross-track

  22. Acquisition of stereoimagesalong-track, only HRS on SPOT 5 Fore-and-aft stereo data collection Derivation of a DEM at resolution of 10 m

  23. SPOT products • Level 1A • radiometric corrections • average location accuracy 350m/50m (SPOT 1 - 4/SPOT 5) • Level 1B • radiometric corrections and systematic geometric corrections • average location accuracy better than 350m/50m • Level 2A • images rectified to UTM/WGS8 system without GCPs, a global DEM used for SPOT 5 images • average location accuracy better than 350m/50m • Level 2B (Precision) • images georeferenced into a given map projection using GCPs • average location accuracy better than 30 m in flat terrain • Level 3 (Ortho) • images georeferenced into a given map projection using GCPs and orthorectified • average location accuracy better than 15m

  24. monitoring urban growth inventorying crops, estimating yields and organizing harvesting Applications of SPOT images detection of a leak on a pipeline

  25. Environmental satellites NOAA • series of polar orbit satellites launched from 1978 • altitude approx. 830 km • collect global data on • cloudcover • surface conditions such as ice, snow, and vegetation • atmospheric temperatures, moisture, aerosol, and ozone distributions

  26. Sensors on NOAA satellites • Advanced Very high Resolution radiometer (AVHRR) • six channels detecting visible, near IR, and thermal IR channels • nominal spatial resolution of 1.1 km at nadir • High Resolution Infrared Radiation Sounder (HIRS) • one visible channel, seven shortwave IR channels, and 12 longwave IR channels • nominal spatial resolution at nadir of 20.3km and 18.9 km • Advanced microwave sounding units (AMSU) • provide measurements for calculating global atmospheric temperature and humidity profiles, vertical water vapor profiles … Among others: Search and Rescue Instruments • program for receiving emergency signals

  27. NOAA’s imagery applications Ozoneprofiles and maps of total ozone values Cloud covers, storms. The image with an original resolution of 1.1km was produced from acomposite of channels 1, 2, and 4 from of the AVHRR instrument. Sea surface temperature map produced from theAVHRR measurements

  28. Links • Earth Observing System http://eospso.gsfc.nasa.gov/ • Landsat http://landsat.gsfc.nasa.gov/ • SPOT http://www.spot.com/html/SICORP/_401_.php • NOAA http://www.oso.noaa.gov/poes/ Literature: Lillesand,T.,M., Kiefer, R., W.: remote sensing and image interpretation, Wiley & Sons, 2000 (2004)

  29. Digital Image Processing • Data Acquisition • Image Rectification and Restoration • geometric and radiometric corrections, noise elimination • Image Enhancement • contrast, filtering, edge enhancement, ... • Image Classification • supervised, unsupervised • Data Merging and GIS Integration • Image Transmission and Compression

  30. Classification • automatically categorisation of all pixels in an image into land cover classes • basic idea: in multispectral images different features types show different combinations of digital numbers • supervised classification • unsupervised • classification stage • determining land cover identity of clusters

  31. Classification algorithms • Supervised classification • training stage (training areas) • classification stage Minimum distance classifier Parallelepiped classifier Maximum likelihood classifier

  32. Axis II Axis I Band 2 Band 1 Principal Component Analysis • images from various wavelength bans appear similar, obtained information is almost the same (interband correlation) • all information contained in an originaln-band data set is compressed to n1<n bands called COMPONENTS • principal component (PC) data values are linear combinations of the original data values • total scene variance of PC1 > PC2 > PC3… • data contained in PCs are uncorrelated (orthogonality)

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