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2009-10 CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 6: ground segment, pre-processing & scanning. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney. Recap. Last week

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2009 10 cegeg046

2009-10 CEGEG046 / GEOG3051Principles & Practice of Remote Sensing (PPRS) 6: ground segment, pre-processing & scanning

Dr. Mathias (Mat) Disney

UCL Geography

Office: 113, Pearson Building

Tel: 7670 0592

Email: mdisney@ucl.geog.ac.uk

www.geog.ucl.ac.uk/~mdisney


2009 10 cegeg046

Recap

  • Last week

    • orbits and swaths

    • Temporal & angular sampling/resolution + radiometric resolution

  • This week

    • data size, storage & transmission

    • pre-processing stages (transform raw data to “products”)

    • sensor scanning mechanisms


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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.)


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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


2009 10 cegeg046

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


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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


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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


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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


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Transmission, storage and processing

  • Ground receiving station, Kiruna, Sweden

From http://www.esa.int/SPECIALS/ESOC/SEMZEEW4QWD_1.html#subhead1


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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


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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/


2009 10 cegeg046

(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


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(Pre?) Processing chain

  • Typically:

    • radiometric calibration

    • radiometric correction

    • atmospheric correction

    • geometric correction/registration


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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.


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Processing chain

  • Typically:

    • radiometric calibration

    • radiometric correction

    • atmospheric correction

    • geometric correction/registration


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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


2009 10 cegeg046

Radiometric correction 9/5/2004

  • 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


2009 10 cegeg046

Fourier domain filtering 9/5/2004

  • 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


2009 10 cegeg046

Processing chain 9/5/2004

  • Typically:

    • radiometric calibration

    • radiometric correction

    • atmospheric correction

    • geometric correction/registration


Remember interactions with the atmosphere

R 9/5/2004

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


2009 10 cegeg046

Atmospheric correction: simple 9/5/2004

  • 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


2009 10 cegeg046

Radiance, L 9/5/2004

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


2009 10 cegeg046

Atmospheric correction: simple 9/5/2004

  • 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


2009 10 cegeg046

Haze due to scan angle of instruments 9/5/2004

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


2009 10 cegeg046

Atmospheric correction: complex 9/5/2004

  • Atmospheric radiative transfer modelling

    • use detailed scattering models of atmosphere including gas and aerosols

      • Second Simulation of Satellite Signal in Solar Spectrum (6s) Vermote et al. (1997)

      • MODTRAN/LOWTRAN (Berk et al. 1998)

      • SMAC Rahman and Dedieu (1994)

      • FLAASH, ACORN, ATREM etc.

http://www-loa.univ-lille1.fr/Msixs/msixs_gb.html

http://geosci.uchicago.edu/~archer/cgimodels/radiation.html


2009 10 cegeg046

Atmospheric correction: complex 9/5/2004

  • 6S radiative transfer model: calculate upward and downward direct and diffuse fluxes

Direct + diffuse reflectance from target (we want) + surroundings 

Transmitted, 

Direct & diffuse from sun 

Path radiance, 

TOA reflectance,  i.e. what we measure

Diffuse (mscatt) between ground and atmos 

ρ* (θs, θv, Δϕ) = Top-of-atmosphere spectral reflectance, as a function of view and sun zenith θs,v and relative azimuth, Δϕ;

tg = total gaseous transmission i.e. solar radiation to surface, then escaping on the way up;

ρa= atmospheric reflectance, function of molecular aerosols optical properties;

τ = atmos. optical depth (e-t/μs and e-t/μv = direct transmittance in sun & view directions, where μs, μv are cos(θs) and cos(θv) respectively;

td(θs), td(θv) = diffuse transmittance in sun & view directions;

ρc= reflectance of target (what we want); ρe = reflectance of surrounding area;

S = spherical (direct + diffuse) albedo of the atmosphere i.e. 1-ρeS accounts for multiple scattering between ground (outside target) and atmosphere…..


2009 10 cegeg046

Atmospheric correction: complex 9/5/2004

  • 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


2009 10 cegeg046

Atmospheric correction 9/5/2004

  • 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


2009 10 cegeg046

Atmospheric correction: summary 9/5/2004

  • 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


2009 10 cegeg046

Processing chain 9/5/2004

  • Typically:

    • radiometric calibration

    • radiometric correction

    • atmospheric correction

    • geometric correction/registration


2009 10 cegeg046

Geometric correction 9/5/2004

  • 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


2009 10 cegeg046

Geometric correction 9/5/2004

  • 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)


2009 10 cegeg046

Corrected to sza = 45° vza = 0 ° 9/5/2004

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


2009 10 cegeg046

Movable sensor head: alter view zen. angle 9/5/2004

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)


2009 10 cegeg046

Pre-processing: summary 9/5/2004

  • 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.


2009 10 cegeg046

Sensor scanning characteristics 9/5/2004

  • 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


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Separate bands 9/5/2004

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


2009 10 cegeg046

Dichroic lens/prism 9/5/2004

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


2009 10 cegeg046

IFOV sweeps surface 9/5/2004

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


2009 10 cegeg046

Sensor motion 9/5/2004

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)


2009 10 cegeg046

Sensor motion 9/5/2004

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)


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Aside: CCD 9/5/2004

  • Charge Couple Device

From http://www.na.astro.it/datoz-bin/corsi?l1a


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Aside: CCD 9/5/2004

  • 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/Products/howccddetectorworks.asp


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Aside: CCD 9/5/2004

  • Si (Silicon) CCD

    • vis/NIR up to ~ 1.1m

  • InGaAs (Indium Gallium Arsenide)

    • IR (~0.9 - 1.6 m)

  • InSb (Indium Antimonide)

    • mid-IR ~3.5 - 4m

  • HgCdTe (Mercury Cadmium Telluride)

    • IR (~10 - 12 m)


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Summary 9/5/2004

  • Ground receiving

    • transfer data from sensor to ground station (storage v. transmission?)

    • can be small-scale these days e.g. MSG, MODIS DB etc.

  • Pre-processing chain

    • atmospheric, geometric correction, radiometric correction and calibration

      • can obtain raw data (level 0 product), some pre-processing (level 1) or fully processed to reflectance, radiance etc. (level 1b/2/3 etc.)

    • then REAL work begins!

  • Scanning mechanisms

    • various depending on application

    • have pros/cons - usual tradeoff of reliability, spatial res. V SNR and geometric distortions (see Lillesand, Kiefer, Chipman section 5.9)

  • Reading

    • Rahman and Dedieu (1994); Vermote et al. (1997)