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Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592

GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) G round segment, pre-processing & scanning. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592 Email: mdisney@ucl.geog.ac.uk http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html

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Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592

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  1. GEOGG141/ GEOG3051Principles & Practice of Remote Sensing (PPRS)Ground segment, pre-processing & scanning Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7679 0592 Email: mdisney@ucl.geog.ac.uk http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html http://www2.geog.ucl.ac.uk/~mdisney/teaching/3051/GEOG3051.html

  2. Recap • Last session • orbits and swaths • Temporal & angular sampling/resolution + radiometric resolution • This session • data size, storage & transmission • pre-processing stages (transform raw data to “products”) • sensor scanning mechanisms

  3. nColumns nColumns (0,0) (0,0) nBands nBands nRows nRows (r,c) (r,c) Time Data volume? • Size of digital image data easy (ish) to calculate • size = (nRows * nColumns * nBands * nBitsPerPixel) bits • in bytes = size / nBitsPerByte • typical file has header information (giving rows, cols, bands, date etc.)

  4. Aside • Several ways to arrange data in binary image file • Band sequential (BSQ) • Band interleaved by line (BIL) • Band interleaved by pixel (BIP) From http://www.profc.udec.cl/~gabriel/tutoriales/rsnote/cp6/cp6-4.htm

  5. Data volume: examples • Landsat ETM+ image? Bands 1-5, 7 (vis/NIR) • size of raw binary data (no header info) in bytes? • 6000 rows (or lines) * 6600 cols (or samples) * 6 bands * 1 byte per pixel = 237600000 bytes ~ 237MB • actually 226.59 MB as 1 MB  1x106 bytes, 1MB actually 220 bytes = 1048576 bytes • see http://www.matisse.net/mcgi-bin/bits.cgi • Landsat 7 has 375GB on-board storage (~1500 images) Details from http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter6/chapter6.htm

  6. Data volume: examples • MODIS reflectance 500m tile (not raw swath....)? • 2400 rows (or lines) * 2400 cols (or samples) * 7 bands * 2 bytes per pixel (i.e. 16-bit data) = 80640000 bytes = 77MB • Actual file also contains 1 32-bit QC (quality control) band & 2 8-bit bands containing other info. • BUT 44 MODIS products, raw radiance in 36 bands at 250m • Roughly 4800 * 4800 * 36 * 2 ~ 1.6GB per tile, so 100s GB data volume per day! Details from http://edcdaac.usgs.gov/modis/mod09a1.asp and http://edcdaac.usgs.gov/modis/mod09ghk.asp

  7. Transmission, storage and processing • Ground segment • receiving stations capture digital data transmitted by satellite • A: direct if Ground Receiving Station (GRS) visible • B: storage on board for later transmission • C: broadcast to another satellite (typically geostationary telecomms) known as Tracking and Data Relay Satellite System (TDRSS) From http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_15_e.html

  8. Transmission, storage and processing • Ground receiving station • dish to receive raw data (typically broadcast in wave) • data storage and archiving facilities • possibly processing occurs at station (maybe later) • dissemination to end users From http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_15_e.html

  9. Transmission, storage and processing • Ground receiving station, Kiruna, Sweden From http://www.esa.int/SPECIALS/ESOC/SEMZEEW4QWD_1.html#subhead1

  10. Transmission, storage and processing • Scale? • can be very small-scale these days • dish or aerial for METEOSAT-type data • desktop PC and some disk space

  11. E.g. MODIS direct broadcast (DB) • MODIS DB • ideal for smaller organisations, developing nations etc. • Only need 3m dish and some hardware • Pre-processing stage can be VERY complex! • Before you let users loose.... From http://daac.gsfc.nasa.gov/DAAC_DOCS/direct_broadcast/

  12. (Pre)Processing chain • Task of turning raw top-of-atmosphere (TOA) radiance values (raw DN) into useful information • geophysical variables, products etc. DERIVED from radiance • Can be very complex, time- (and space) consuming • BUT pre-processing determines quality of final products • e.g. reflectance, albedo, surface temperature, NDVI, leaf area index (LAI), suspended organic matter (SOM) content etc. etc. • typically require ancillary information, models etc. • combined into algorithm for turning raw data into information

  13. (Pre?) Processing chain • Typically: • radiometric calibration • radiometric correction • atmospheric correction • geometric correction/registration

  14. DNout DNin Radiometric calibration • Account for sensor response • cannot assume sensor response is linear • account for non-linearities via pre-launch and/or in-orbit calibration • On-board black body (A/ATSR), stable targets (AVHRR), inter-sensor comparisons etc.

  15. Processing chain • Typically: • radiometric calibration • radiometric correction • atmospheric correction • geometric correction/registration

  16. CHRIS-PROBA image over Harwood Forest, Northumberland, UK, 9/5/2004 Radiometric correction • Remove radiometric artifacts • dropped lines • detectors in CCD may have failed • fix by interpolating DNs either side? • Automate? • Topographic effects? See http://www.chris-proba.org.uk

  17. Radiometric correction • Remove radiometric artifacts • striping • deterioration of detectors with time (& non-linearities) • Filter in Fourier domain to remove periodic striping From http://visibleearth.nasa.gov/cgi-bin/viewrecord?7386

  18. Fourier domain filtering • Filter periodic noise/aretfacts Fourier transform (to freq. domain) Convolve with Fourier domain filter Apply inverse FT From http://homepages.inf.ed.ac.uk/rbf/HIPR2/freqfilt.htm

  19. Processing chain • Typically: • radiometric calibration • radiometric correction • atmospheric correction • geometric correction/registration

  20. R R 2 1 target target R 4 R 3 target target Remember? Interactions with the atmosphere • Notice that target reflectance is a function of • Atmospheric irradiance (path radiance: R1) • Reflectance outside target scattered into path (R2) • Diffuse atmospheric irradiance (scattered onto target: R3) • Multiple-scattered surface-atmosphere interactions (R4) From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf

  21. Atmospheric correction: simple • So....need to remove impact of atmosphere on signal i.e. turn raw TOA DN into at-ground reflectance • Simple methods? • Convert DN to apparent radiance Lapp – sensor dynamic range • Convert Lapp to apparent reflectance (knowing response of sensor) • Convert to intrinsic surface property - at-ground reflectance in this case, by accounting for atmosphere

  22. Radiance, L Offset assumed to be atmospheric path radiance (plus dark current signal) Regression line L = G*DN + O (+) DN Target DN values Atmospheric correction: simple • Simple methods • e.g. empirical line correction (ELC) method • Use target of “known”, low and high reflectance targets in one channel e.g. non-turbid water & desert, or dense dark vegetation & snow • Assuming linear detector response, radiance, L = gain * DN + offset • e.g. L = DN(Lmax - Lmin)/255 + Lmin Lmax Lmin

  23. Atmospheric correction: simple • Drawbacks • require assumptions of: • Lambertian surface (ignore angular effects) • Large, homogeneous area (ignore adjacency effects) • Stability (ignore temporal effects) • Also, per-band not per pixel so assumes • atmospheric effects invariant across image • illumination invariant across image • ok for narrow swath (e.g. airborne) but no good for wide swath

  24. Haze due to scan angle of instruments Airborne Thematic Mapper (ATM) data over Harwood Forest, Northumberland, UK, 13/7/2003 Compact Airborne Spectrographic Imager (CASI) data over Harwood Forest, Northumberland, UK, 13/7/2003 Example: airborne data See: http://www.nerc.ac.uk/arsf

  25. Atmospheric correction: complex • Atmospheric radiative transfer modelling • use detailed scattering models of atmosphere including gas and aerosols • Second Simulation of Satellite Signal in Solar Spectrum (6s) • MODTRAN/LOWTRAN • SMAC etc. http://www-loa.univ-lille1.fr/Msixs/msixs_gb.html http://geosci.uchicago.edu/~archer/cgimodels/radiation.html

  26. Atmospheric correction: complex • Radiative transfer models such as 6S require: • Geometrical conditions (view/illum. angles) • Atmospheric model for gaseous components (Rayleigh scattering) • H2O, O3, aerosol optical depth,  (opacity) • Aerosol model (type and concentration) (Mie scattering) • Dust, soot, salt etc. • Spectral condition • bands and bandwidths • Ground reflectance (type and spectral variation) • surface BRDF (default is to assume Lambertian….) • If no info. use default values (Standard Atmosphere) From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf

  27. Atmospheric correction • Can measure  from ground and/or use multi-angle viewing to obtain different path lengths through atmos e.g. MISR, CHRIS • infer optical depth and path radiance AND aerosols • so use data themselves to infer atmos. scattering From:http://visibleearth.nasa.gov/cgi-bin/viewrecord?129

  28. Atmospheric correction: summary • Convert TOA radiance to at-ground reflectance • VERY important to get right (can totally dominate signal) • Simple methods • e.g. ELC but rough and ready and require many assumptions • Complex methods • e.g. 6S but require much ancillary assumptions • BUT can use multi-angle measurements to correct • i.e. treat atmosphere as PART of surface parameter retrieval problem • different view angles give different PATH LENGTH

  29. Processing chain • Typically: • radiometric calibration • radiometric correction • atmospheric correction • geometric correction/registration

  30. Geometric correction • Account for distortion in image due to motion of platform and scanner mechanism • Particular problem for airborne data: distortion due to roll, pitch, yaw From:http://liftoff.msfc.nasa.gov/academy/rocket_sci/shuttle/attitude/pyr.html

  31. Geometric correction • Airborne data over Barton Bendish, Norfolk, 1997 • Resample using ground control points • various warping and resampling methods • nearest neighbour, bilinear or bicubic interpolation.... • Resample to new grid (map)

  32. Corrected to sza = 45° vza = 0 ° AVHRR bands 1 & 2 uncorrected BRDF effects? • Multi-temporal observations have varying sun/view angles • To compare images from different dates, need same view/illum. conditions i.e. account for BRDF effects • fit BRDF model & use to normalise reflectance e.g. to nadir view/illum. • e.g. MODIS NBAR nadir BRDF-adjusted reflectance (http://geography.bu.edu/brdf/userguide/nbar.html) From:http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/landcov/corr/brdf_e.html

  33. Movable sensor head: alter view zen. angle Azimuthal rail: alter view azimuth angle BRDF effects? • Field measurements of BRDF: goniometer e.g. European Goniometric Facility (EGO) at JRC, & FIGO in CH • http://www.geo.unizh.ch/rsl/research/SpectroLab/goniometry/index.shtml ASIDE: Chapter (12) in Liang (2004) book on validation, sampling; Also Jensen chapter (11)

  34. Pre-processing: summary • Convert raw DN to useful information • calibrate instrument response and remove radiometric blunders • remove atmospheric effects • remove BRDF effects? • resample onto grid • Results in more fundamental property e.g. surface reflectance, emissivity etc. • NOW apply scientific algorithm to convert reflectance to LAI, fAPAR, albedo, ocean colour etc. etc. etc.

  35. Sensor scanning characteristics • Range of scanning mechanisms to build up images • Different applications, different image characteristics and pros/cons for each type • scanning mechanisms: electromechanical • discrete detectors • whiskbroom scanners • pushbroom scanners • digital frame cameras

  36. Separate bands Lens Scan mirror Sensor path Dichroic mirrors Discrete detectors • Mirror can rotate or scan • individual detectors record signal in different bands • How do we split signal into separate bands? • Dichroic mirror or prism Adapted from Jensen, 2000, p. 184

  37. Dichroic lens/prism Sensor motion Scanning mechanisms: across track • 3 main types of electromechanical (detectors, optics plus mechanical scanning) mechanisms • across track or “whiskbroom” scanner (mechanical) • linear detectors array (electronic) • beam splitter / dichroic / prism / filters splits incoming signal into separate wavelength regions From Jensen, J. (2000) Remote sensing: and Earth resource perspective, p. 184

  38. IFOV sweeps surface Scanning mechanisms: across track • Whiskbroom scanner • Mirror either rotates fully, or oscillates • Oscillation can have delays at either end of scan (vibration?) • Restricted “dwell time” requires tradeoff with no. of bands to give acceptable SNR • motion of platform and mirror causes image distortion • Diameter of IFOV on surface  H • H = flying height;  = nominal angular IFOV in radians • e.g. For 2.5 mrad IFOV, H = 3000m, D = 2.5x10-3x3000 = 7.5m • Typically .5 to 5 mrad - tradeoff of spatial resolution v SNR Adapted from Lillesand, Kiefer and Chipman, 2004 p. 332 Examples: Landsat MSS, TM and ETM, AVHRR, (MODIS) See Jensen Chapter 7

  39. Sensor motion Sensor motion Scanning mechanisms: along track • Pushbroom scanner • pixels recorded line by line, using forward motion of sensor • less distortion across track but overlap to avoid gaps • No moving parts so less to go wrong and longer “dwell time” • BUT needs v. good calibration to avoid striping • Ground-sampled distance (GSD) in x-track direction fixed by CCD element size • GSD along-track fixed by detector sampling interval (T) used for AD conversion Examples: SPOT HRVIR and Vegetation, MISR, IKONOS, QuickBird See Jensen Chapter 7 From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & J. Jensen (2000)

  40. Sensor motion Scanning mechanisms • Central perspective / digital frame camera area arrays • Multitple CCD arrays • Silicon (vis/NIR), HgCdTe (SWIR/LWIR)? • Similar image distortion to film camera • distortion increases radially away from focal point From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & Jensen (2000)

  41. Scanning mechanisms: examples • Discrete detectors and scanning mirrors • Landsat MSS, TM, ETM+, NOAA GOES, AVHRR, ATSR • Multispectral linear arrays • SPOT (1-3) HRV, HRVIR & SPOT-VGT, IKONOS, ASTER & MISR (both on board NASA Terra) • Imaging spectrometers using linear and area arrays • AVIRIS, CASI, MODIS (on NASA Terra and Aqua) From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & Jensen (2000)

  42. MODIS scan mirror http://modis.gsfc.nasa.gov/about/scanmirror.html • Continuously rotating and double-sided • SEVIRI (Spinning Enhanced Vis and IR Imager) on board MSG • Whole satellite rotates • Vertical scan plus rotation = image Scanning mechanisms: examples

  43. nr line r Along track pixel Frame Across track Scanning mechanisms: continued • Image frame created by scanning detector footprint • n pixels per line, pixel size r * r • Along track speed v ms-1 so footprint travels distance r in r/v secs •  One line of data must be acquired in <= r/v secs • Typical v? • Orbital period T ~ 100 mins, Earth radius ~ 6.4x103m • v = 2*6.4x103 / 100*60 = 6.7x103ms-1

  44. X-track scan (whiskbroom) Start rv Platform has moved r in rv secs Scanning mechanisms: single detector • Even if we obtain 1 line in r/v secs say..... • Significant along-track displacement from start to end of x-track scan line

  45. Active scan r/2 r flyback Speed, v Scanning mechanisms: single detector • Zig-zag mechanism • active scan lasts r/2v secs • n pixels per line, so “dwell time” (seconds per pixel) is r/2nv secs/pixel • ok for low res e.g. AVHRR, as large r • But problems for mod - high res. • E.g. Landsat MSS, r = 70m, v = 7x103ms-1 n=3000 so dwell time = 70/2*3000*7x103 = 1.7secs (OK for SNR) • BUT with single detector, required length of scan cycle r/v is 10msecs (70/7x103) • = 100 scan cycles per second • TOO FAST!

  46. T = 0 WEST T = 53ms Active scan 474m EAST T = 73.4ms 185km (swath width) Scanning mechanisms: e.g. MSS • MSS has 4x6 array of receptors - 4 bands, 6 receptors per band • 6 lines scanned simultaneously • ‘footprint’ of single receptor follows a zig-zag track • ~30 cycles per second

  47. Active r Active Active Speed, v Scanning mechanisms: boustrophedon • Alternative right  left, left  right • 2 n line pixels scanned in r/v secs •  r/2nv secs/pixel • For TM for e.g. r = 30m, v = 7x103ms-1, n = 6000 pixels •  dwell time 0.36 sec (not long enough for good SNR) • scan cycle ~4.3msecs(>220 s-1) • Way too fast i.e. single detector operation inadequate for TM • use 6 detectors per band (vis), and 16 lines at a time in vis, 4 at a time in thermal • 100 detectors total From: http://rst.gsfc.nasa.gov/Intro/Part2_20.html

  48. Aside: CCD • Charge Couple Device From http://www.na.astro.it/datoz-bin/corsi?l1a

  49. Aside: CCD • Photons arrive (through optics and filters) and generate free electrons in CCD elements (few x106 on a CCD) • More photons == more electrons collected • Charge coupling: CCD design allows all packets of charged electrons to be moved one row at a time by varying voltage of adjacent rows across CCD - cascade effect • i.e. Count is done at one point (lower corner) – so delay due to read time • http://electronics.howstuffworks.com/digital-camera2.htm • http://www.oceanoptics.com/technical/operatingprinciples.asp

  50. Aside: CCD • Si (Silicon) CCD • vis/NIR up to ~ 1.1μm • InGaAs (Indium Gallium Arsenide) • IR (~0.9 - 1.6 μm) • InSb (Indium Antimonide) • mid-IR ~3.5 - 4μm • HgCdTe (Mercury Cadmium Telluride) • IR (~10 – 12 μm)

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