Preprocessing in Remote Sensing. Introduction Geo Information (GRS 10306)

Transcription

1 Preprocessing in Remote Sensing Lammert Kooistra Contact: Introduction Geo Information (GRS 10306)

2 The art of remote sensing source: ASTER satellite (earthobservatory.nasa.gov)

3 Disturbing factors in RS acquisition I (raw data) source: DAIS airborne sensor, area Germany,

4 Disturbing factors in RS acquisition II source: ASTER satellite, north of Netherlands, 2001 source: Landsat TM, Indonesia uncorrected corrected

5 Disturbing factors in RS acquisition III unsupervised classification source: HyMap airborne sensor, Millingerwaard floodplain, 2004

6 Disturbing factors in RS acquisition III examples of striping, due to non-identical detector response caused by: detector characteristics changes with time / rise of temperature detector failure source: AHS airborne sensor, 2005 source: Landsat TM

7 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: shrimp farming in Ecuador Example: monitoring landcover: urban sprawl 2000 (left) 2003 (right) source:

8 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 analysis variables/ products application Remote sensing processing chain

9 Radiance at the top of atmosphere Pathway A: direct reflected sunlight Pathway B: skylight Pathway C: air light Atmospheric influence in two directions (up and down) A B C Generalized overview of pathways from sun to remote sensing sensor

10 Spectral preprocessing chain field reflectance true upwelling spectral radiance at TOA field measurement of reflectance radiometric calibration measured DNs at sensor satellite based measurement of reflectance calibrated radiance at sensor atmospheric correction ground reflectance (from: Ustin et al., 2004)

11 radiometric calibration sensor calibration before launch: from DN to physical units Pre launch calibration Correction factors for all channels: offset (A 0 ) gain (A 1 ) MERIS pre-launch calibration in laboratory DN = A 0 + A 1 * L L = measured (in lab) radiance

12 The launch of a satellite sensor ESA s Envisat platform Indian Geo-synchronous Satellite Launch Vehicle (GSLV)

13 radiometric calibration sensor calibration after launch: on board calibration: Internal lamps and reference panels vicarious calibration: 1. Very accurate ground measurements 2. Large homogeneous ground units 3. Compare ground and satellite radiance MERIS onboard calibration set-up

14 radiometric correction scene illumination: sun elevation correction earth sun distance correction standard procedures for correction Sun elevation difference per season

15 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 (

16 Spectral preprocessing chain field reflectance true upwelling spectral radiance at TOA field measurement of reflectance radiometric calibration measured DNs at sensor satellite based measurement of reflectance calibrated radiance at sensor atmospheric correction ground reflectance (from: Ustin et al., 2004)

17 Atmospheric correction Two main processes: Scattering: reflector Absorption: energy reduction A B C Two main approaches: Simple methods: often statistical Complex radiative transfer based methods (incl. use of meteorological data)

18 The atmosphere: scattering Scattering: 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 large particles like dust, smoke and rain Rayleigh scattering causing a reddened sky at sunset

19 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 µm 1mm 1cm 1m 10m absorption atmospheric transmittance UV VIS NIR MIR MIR TIR TIR blocking effect of atmosphere microwaves Centre position of Landsat TM bands wavelength (µm)

20 Atmospheric correction: examples Atmospheric correction of Landsat TM images (Liang et al., 2001) before after

21 Geometric correction Sources 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: ground control points (&DEM) GCP resampling Image to Image resampling

22 Geometric correction Additional geometric distortions for airborne images: Variations in aircraft/platform altitude, velocity and attitude: pitch roll yaw (from:schott, 1996)

23 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

24 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/

25 Product levels acquisition Level 0 Product raw data Laboratory Calibration (radiometric and spectral), Vicarious Validation System Correction & Radiometric Calibration Level 1 Product at-sensor radiance data Attitude Data, Position Data, DEM Radiative Transfer Model, Atmospheric Variables, Topographic Variables Geometric Correction Atmospheric Correction Level 2a Product ortho-rectified data (specified accuracy) Level 2b Product Atm. corrected data preprocessing Level 2 Product ortho-rectified atm. corrected data image analysis variables and application statistical or physical models & validation Level 3 Product thematic variables mapped on uniform space-time grid scales

26 Example EO product MODIS Global Landcover 2000 Forest burning aug-oct 2000 Global LAI July 2006 Surface reflectance June 8,

27 Example EO product level 3+ Published on main page of Volkskrant 15 september 2006 Source: NASA Icesat mission (icesat.gsfc.nasa.gov): images of North Pole

28 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

29 Relevant web sources Background on calibration and validation bin/qa_www/newpage.cgi EO data portals