dr. mathias (mat) disney ucl geography office: 113, pearson building tel: 7679 0592

UCL DEPARTMENT OF GEOGRAPHYUCL DEPARTMENT OF GEOGRAPHY

GEOGG141/ GEOG3051Principles & Practice of Remote Sensing (PPRS)Ground segment, pre-processing & scanning

Dr. Mathias (Mat) DisneyUCL GeographyOffice: 113, Pearson BuildingTel: 7679 0592Email: [email protected]://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.htmlhttp://www2.geog.ucl.ac.uk/~mdisney/teaching/3051/GEOG3051.html

http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html

http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html

http://www2.geog.ucl.ac.uk/~mdisney/teaching/3051/GEOG3051.html

UCL DEPARTMENT OF GEOGRAPHY

2

• 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

Recap


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• 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.)

Data volume?

(0,0)nColumns

nRow

s

(r,c)

nBands(0,0)

nColumns

nRow

s

(r,c)

nBands

Time


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• Several ways to arrange data in binary image file– Band sequential (BSQ)– Band interleaved by line

(BIL)– Band interleaved by pixel

(BIP)

Aside

From http://www.profc.udec.cl/~gabriel/tutoriales/rsnote/cp6/cp6-4.htm


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• 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)

Data volume: examples

Details from http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter6/chapter6.htm


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• 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!

Data volume: examples

Details from http://edcdaac.usgs.gov/modis/mod09a1.asp and http://edcdaac.usgs.gov/modis/mod09ghk.asp


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• 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)

Transmission, storage and processing

From http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_15_e.html


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• 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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• Ground receiving station, Kiruna, Sweden


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


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• 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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• 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....

E.g. MODIS direct broadcast (DB)

From http://daac.gsfc.nasa.gov/DAAC_DOCS/direct_broadcast/


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(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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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.

DNout

DNin


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Processing chain

• Typically:– radiometric calibration– radiometric correction– atmospheric correction– geometric correction/registration


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Radiometric correction• Remove radiometric

artifacts– dropped lines

• detectors in CCD may have failed

– fix by interpolating DNs either side?

– Automate?• Topographic effects?

CHRIS-PROBA image over Harwood Forest, Northumberland, UK, 9/5/2004

See http://www.chris-proba.org.uk


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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


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Fourier domain filtering• Filter periodic noise/aretfacts

From http://homepages.inf.ed.ac.uk/rbf/HIPR2/freqfilt.htm

Fourier transform (to freq. domain)

Convolve with Fourier domain filter

Apply inverse FT


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Processing chain• Typically:

– radiometric calibration– radiometric correction– atmospheric correction– geometric correction/registration


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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

R1

target

R2

target

R3

target

R4

target


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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


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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

DN

Radiance, L

Target DN values

Regression line L = G*DN + O (+)

Offset assumed to be atmospheric path radiance (plus dark current signal)

Lmax

Lmin


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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


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Example: airborne data

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

See: http://www.nerc.ac.uk/arsf

Haze due to scan angle of instruments


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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


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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


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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


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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


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Processing chain

• Typically:– radiometric calibration– radiometric correction– atmospheric correction– geometric correction/registration


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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


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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)


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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)

AVHRR bands 1 & 2 uncorrected

From:http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/landcov/corr/brdf_e.html

Corrected to sza = 45° vza = 0 °


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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

Movable sensor head: alter view zen. angle

Azimuthal rail: alter view azimuth angle

ASIDE: Chapter (12) in Liang (2004) book on validation, sampling; Also Jensen chapter (11)


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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.


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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


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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

Lens

Scan mirror

Sensor path

Separate bands

Dichroic mirrors


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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

Sensor motion

Dichroic lens/prism


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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

IFOV sweeps surface

Adapted from Lillesand, Kiefer and Chipman, 2004 p. 332

Examples: Landsat MSS, TM and ETM, AVHRR, (MODIS)See Jensen Chapter 7


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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

From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & J. Jensen (2000)

Sensor motion

Sensor motion

Examples: SPOT HRVIR and Vegetation, MISR, IKONOS, QuickBirdSee Jensen Chapter 7


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Scanning mechanisms

From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & Jensen (2000)

Sensor motion

• 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


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Scanning mechanisms: examples

From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & Jensen (2000)

• 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)


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Scanning mechanisms: examples

• 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


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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

pixel

rline

Frame

Along trac k

Across track

nr


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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

Start

Platform has moved r in rv secs

X-track scan (whiskbroom)

rv


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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 = 1.7secs (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!

Active scan

r flyback

Speed, v

r/2


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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

474m

T = 0

T = 73.4ms

Active scanT = 53ms

WEST

EAST

185km (swath width)


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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 = (20/3) x 103ms-

1 n = 6000– dwell time 0.38 sec (not long enough

for good SNR)– scan cycle ~4.5 msecs (~220 per second)– 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

Activer

Speed, v

Active

Active

From: http://rst.gsfc.nasa.gov/Intro/Part2_20.html


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Aside: CCD• Charge Couple Device

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


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Aside: CCD

• http://electronics.howstuffworks.com/digital-camera2.htm• http://www.oceanoptics.com/Products/

howccddetectorworks.asp

• 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


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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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Summary• 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)

dr. mathias (mat) disney ucl geography office: 113, pearson building tel: 7679 0592

Documents

raw data

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digital data

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