PREPROCESSING

IN REMOTE SENSING

Introduction Geo�Information Science (GRS�10306)

The art of remote sensing

source: ASTER satellite (earthobservatory.nasa.gov)

Disturbing factors in RS acquisition I (raw data)

source: DAIS airborne sensor, area Germany, 2000

http://www.dlr.de/caf/anwendungen/umwelt/abb_spektroskopie/qlooks/qlooks05/_qlooks05/qlooks05_en.htm

Disturbing factors in RS acquisition IIsource: ASTER satellite, north of Netherlands, 2001

source: Landsat TM, Indonesia

uncorrected corrected

Disturbing factors in RS acquisition III

source: HyMap airborne sensor, Millingerwaard floodplain, 2004

unsupervised classification

Disturbing factors in RS acquisition IV

source: AHS airborne sensor, 2005

examples of striping, due to non-identical detector response caused by:• detector characteristics• changes with time / rise of temperature• detector failure

source: Landsat TM

Which factors influence RS image acquisition?� Sensor characteristics� Atmosphere, weather� Earth surface (geometry)� Acquisition method: satellite or airborne� Others: …

� However, are Remote Sensing images comparable:� in time (e.g., monitoring )� between sensors (e.g., MODIS and Landsat TM)

Example: monitoring landcover: urban sprawl 2000 (left) – 2003 (right)

source: www.lgn.nl

Example: shrimp farming in Ecuador

Preprocessing in RS chain� Preprocessing = preparatory phase to improve image

quality as a basis for further analysis

� Most common steps:� Radiometric calibration: from DN to physical unit (radiance or

reflectance)� Atmospheric correction� Geometric correction

acquisition preprocessing image analysisvariables/products

application

Remote sensing processing chain

Radiance at the top of atmosphere

A B

C� Pathway A: direct reflected sunlight� Pathway B: skylight� Pathway C: air light

� Atmospheric influence in two directions (up and down)

Generalized overview of pathways fromsun to remote sensing sensor

Spectral preprocessing chain

(from: Ustin et al., 2004)

field reflectance ‘true’ upwellingspectral radiance

at TOA

measured DNsat sensor

calibrated radianceat sensor

ground reflectance

radiometric calibration

atmospheric correction

field measurementof reflectance satellite based

measurementof reflectance

Radiometric calibration

sensor calibration before launch:

� from DN to physical units� Pre-launch calibration

� Correction factors for all channels:

� offset (A0)� gain (A1)

� DN = A0 + A1 * L

� L = measured (in lab) radiance

MERIS pre-launch calibration in laboratory

The launch of a satellite sensor

Indian Geo-synchronous Satellite Launch Vehicle (GSLV)

ESA’s Envisat platform

Radiometric calibrationsensor calibration after launch:

� on-board calibration: � Internal lamps and reference panels

� vicarious calibration:1. Very accurate ground measurements2. Large homogeneous ground units3. Compare ground and satellite radiance

MERIS onboard calibration set-up

1. 2. 3.

Radiometric correction

� scene illumination:� sun elevation correction� earth-sun distance correction� standard procedures for correction

Sun elevation difference per season

Radiometric correction: image noise� image noise:

� striping� bit errors � line drop, e.g., for Landsat TM7

due to failure of the Scan Line Corrector (SLC)

� specialized procedures for correction

Landsat TM7, central Netherlands, august 2006

(http://edcsns17.cr.usgs.gov/EarthExplorer/)

Spectral preprocessing chain

(from: Ustin et al., 2004)

field reflectance ‘true’ upwellingspectral radiance

at TOA

measured DNsat sensor

calibrated radianceat sensor

ground reflectance

radiometric calibration

atmospheric correction

field measurementof reflectance satellite based

measurementof reflectance

Atmospheric correction

Two main processes:� Scattering: reflector� Absorption: energy reduction

� Two main approaches:� Simple methods: often statistical� Complex radiative transfer based

methods (incl. use of meteorological data)

A B

C

The atmosphere: scatteringScattering: disturbance of EM waves by constituents of the atmosphere

resulting in change of direction and spectral distribution of the EM energy

� Rayleigh scattering: caused by particles much smaller than the wavelength of the light (e.g., air molecules): wavelength dependent

� Mie scattering: caused by influence of (spherical) aerosol particles on radiance

� Non-selective scattering: influence of largeparticles like dust, smoke and rain

Rayleigh scattering causing a reddened sky at sunset

The atmosphere: absorption

� Absorption: EM energy is taken up by atmospheric components

� Absorption is wavelength specific� Choice of bands for EO sensors

within atmospheric windows

200 µµµµm0.3 0.6 1.0 5.0 10 50 10m100 1mm 1cm 1m

mic

row

avesblocking effect of atmosphere

atm

osp

her

ictr

ansm

itta

nce

UV

NIR

MIR

MIR

TIR TIR

VIS

wavelength (µm) ab

sorp

tion

Centre position of Landsat TM bands

Atmospheric correction: examples

before

after

Atmospheric correction of Landsat TM images (Liang et al., 2001)

Geometric correctionSources of geometric distortions of images:� Curvature of the earth� Earth rotation under the sensor while image is acquired� Panoramic distortion due to the field of view of the sensor� Topography of the terrain

Systematic distortions:� Mostly (automatic) corrected before

image is delivered by ground station

Random distortions:� Corrected by using GCP & DEM

�GCP resampling�Image to Image resampling

Geometric correction

Additional geometric distortions for airborne images:

� Variations in aircraft/platform altitude, velocity and attitude:� pitch� roll� yaw

(from:Schott, 1996)

Quality assessment in RS chain

� Visual: both image and spectrum

� Statistics: e.g., histogram� Validation: compare real measured values with

RS derived variables

� Standards for pre-processing

Current developments� Automatic preprocessing facilities� Standardization of data levels (MODIS) � Mosaics combine different days to get complete image

source: edcimswww.cr.usgs.gov/pub/imswelcome/

Product levels

Level 1 Product

System Correction &Radiometric Calibration

Level 0 Product

Level 2 Product

Laboratory Calibration(radiometric and spectral), Vicarious Validation

Attitude Data, Position Data, DEM

Radiative Transfer Model,Atmospheric Variables,Topographic Variables

Level 2a Product

raw data

at-sensor radiance data

ortho-rectified atm. corrected data

Level 2b ProductAtm. corrected data

ortho-rectified data (specified accuracy)

Geometric Correction

Atmospheric Correction

statistical or physical models& validation

Level 3 Product thematic variables mapped on uniform space-time grid scales

preprocessing

image analysis

acquisition

variables and application

Example EO product MODIS

http://landweb.nascom.nasa.gov/cgi-bin/browse/browse.cgi

Forest burning aug-oct 2000

Global Landcover 2000Global LAI July 2006

Surface reflectance June 8, 2000

Example EO product level 3+Published on main page of Volkskrant15 September 2006

Source: NASA Icesat mission (icesat.gsfc.nasa.gov): images of North Pole

Summary� Preprocessing essential step in remote sensing processing chain

(e.g., monitoring)

� Two main preprocessing steps:� Radiometric calibration: DN to physical units� Correction of (known) distortions: geometry and atmosphere� In addition: other errors (striping, line drop etc.)

� Large number of preprocessing methods: choice depends on:� Accuracy requirements of end-user� Available ground data and e.g., meteorological data

� Current development: � standardization of preprocessing steps and products� automated processing chains� online access of different product levels

Study material

Theory Reader Remote SensingChapter 4

Practical Internet-exercise Preprocessing

Relevant web sources

Background on calibration and validation� http://landsathandbook.gsfc.nasa.gov/� http://www.ncaveo.ac.uk/� http://landweb.nascom.nasa.gov/cgi-bin/QA_WWW/newPage.cgi

EO data portals� http://edcimswww.cr.usgs.gov/pub/imswelcome/� http://skgr0103.wur.nl/~geodesk/CGIRSC/catalogue/CGI%20RSC1.htm� http://glcf.umiacs.umd.edu/index.shtml