radiometric correction.ppt

7/22/2019 radiometric correction.ppt

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Radiometric Correction of

Image Data



Radiometric Correction

Atmospheric correction attempts toquantify (i.e., remove) the effect of theatmosphere at the time an image was

acquired Geometric correction improves the

fidelity of pixel-DN location in an image

Radiometric correction improves thefidelity of the DN‟s that constitute an image

The purpose of radiometric correctionis to reduce the errors that may

confound scientific use of the DN’s



Sources of Radiometric Noise

Radiometric errors constitute the “noise”in a remotely sensed signal (i.e., imagevariations not associated with actualvariations in the ground target of interest)

The errors include sensor related effects(e.g., electronic noise, dropped scan lines)

Spatial and/or temporal variations in thequantity or quality of illumination

(incoming irradiance) Surface properties (e.g., topographic

effects, sun glint)



Reasons for Radiometric Correction

Correct for inconsistencies in image DN‟scaused by sensor errors or environmentalnoise

Normalize DN‟s between / among spectralchannels in the same image

Normalize DN‟s between / among multi-temporal images

Retrieve surface-energy properties suchas reflectance, albedo, groundtemperature, or other parameters

associated with scientific units of measure



Errors Due to Sensor Problems Line Dropout

Striping or banding (detector out ofadjustment)

Line Start problems

Sensor Saturation

“Noisyimage”

Correctedimage



Errors Due to Sensor Problems

Offset scan lines



Landsat-7 Scan Line Corrector Failure

Error firstnoticedduringMay, 2003

Landsat-7 image of Railroad Valley, NV

http://spaceflightnow.com/news/n0307/27landsat7 /

Pre-SLCanomaly

Post-SLCanomaly

Post-SLCanomalyaftercorrectionalgorithm



Scene-Illumination Adjustment

Scene Illumination• In satellite remote sensing, imagery

acquired at different times of the yearmay be required (e.g., to studyphenological cycle)

• These may require sun elevationcorrection and an earth-sun distance

correction

• Sun elevation correction accounts forthe seasonal position of the sunrelative to the earth

Image data acquired under differentsolar illumination angles need to benormalized to a constant solar position

Correction (next slide) ignorestopographic and atmospheric effects



Correction for Differential Illumination

Solar Elevation: the angularelevation of the sun above thehorizon

Solar Zenith Angle: the angulardeviation from directly overhead (orthe complement of elevation)

Corrections:

DN ____

Sine Elevation Angle

orDN ____

Cosine Zenith Angle

This procedure normalizes the data to an overhead sun



Scene-Illumination Adjustment

• Earth-Sun Distance Correction

Normalize for seasonal changes

in distance between earth & sun The irradiance from the sun

decreases as the square of theearth-sun distance

Earth-sun

distance during:

Aphelion: 94.8 million miles

(152.6 million kilometers)

Perihelion: 91.7 million

miles (147.5 millionkilometers)



The DN’s Comprising Landsat Images

Following the launch of the Landsat-MSS (1972), many research andapplications projects were conducted

(and published) using raw DN‟s

Over time, investigators began tothink about the pixel-brightness

values (DN‟s). Questions followed….



Questions About Image DN’s

What are they (and what aren‟tthey)?

Is it valid to compare DN‟s fromseveral different satellite systems(whether Landsat-5 versus Landsat-7or Landsat-7 versus Spot-5 or other

combinations)?

Is it valid to compare image datasetsfrom one time period to the next?

How do we make DN‟s truly useful?



Answers to the Questions About DN’s

The DN‟s are unit-less numbers that serveas surrogates for target radiance. In fact,the DN‟s are linear with radiance, but

there is no scientific unit of measureassociated with them.

For scientific use, one must convert theDN‟s to some real physical unit (i.e.,

containing a meaningful unit of measure)

For example, the DN‟s can be converted toradiance (mW cm-2 sr-1) and/or

reflectance (%)



The article thatcalled attentionto the problemof image DN‟s,with a focus onLandsat

1982



LMax isradiancemeasured atdetectorsaturation

LMin is thelowest radiancemeasured bythe detector

QCalMax is themaximum DNpossible



Publishedtables for usein converting

Landsat DN‟sto physicalvalues



An Example of aProject Involving

the Normalizing ofImages to Cover aLarge Study Area

No conversion of DN‟sto radiance orreflectance wasnecessary

Rather, the need was

to normalize theradiometry in the 20Landsat-TM imagesto be used in amosaic and also forchange detection over

time



Scene 1 Selection

Path/Row Optimal Period Dominant Land Cover (%) Selected Scenes (circa 1990) Selected Scenes (circa 2000)

19/36 May7-June17 Forest (76.95) 1988 May 30 2000 May 31

19/37 Apr23-June3 Forest (73.41) 1990 June 5 2001 May 18

19/38 Apr23-June17 Forest (56.18) 1991 June 8 2003 Apr 14

18/38 Apr23-June17 Forest (36.55) 1990 May 29 2000 Apr 30

19/39 Apr23-July1 Forest 42.38) 1990 Apr 18 2000 Apr 18

Scene 2 Selection

Path/Row Optimal Period Dominant Land Cover (%) Selected Scenes (circa 1990) Selected Scenes (circa 2000)

19/36 Sep24-Dec31 Forest (76.95) 1991 Sep 28 2000 Sep 28

19/37 Sep24-Dec32 Forest (73.41) 1991 Sep 28 2001 Oct 1

19/38 Oct8-Dec2 Forest (56.18) 1986 Oct 16 2001 Oct 14

18/38 Oct8-Dec31 Forest (36.55) 1991 Nov 24 2000 Oct 23

19/39 Nov5-Jan28 Forest 42.38) 1990 Nov 12 2000 Dec 17

Selected Landsat Scenes



Radiometric Normalization of

Landsat Data

After visual interpretation, adjacentscenes appeared to have different ranges

of brightness values The variation is likely due to differing

atmospheric and illumination conditions

A radiometric normalization process was

used to correct the anomalies There was a need to adjust the brightness

values in each band to approximately thesame radiometric scale.



Methods for Radiometric Normalization

Identify a „master scene‟ First order atmospheric correction (haze

reduction) Within areas of overlap between „master‟ and

„slave‟ scenes, identify pseudoinvariant features(PIF) Difference overlapping area between „master‟ and

„slave‟ scenes Calculate mean difference for each band based

on PIF‟s Apply the mean difference, (bias value) to the

entire slave sceneAdopted from Homer et al. (1997)



Pseudoinvariant Feature Selection PIF‟s are targets that remain spectrally stable through time.

Examples include paved areas, rooftops, deep non-turbidwater, and dense evergreen forests.

Selection of 2 PIF‟s between common overlapping area of master and slavescene.



Difference of Overlap Area

Band1 master - Band1 slave = Band1 difference



Mean Difference of Overlap Area

The mean difference should ONLY be calculated using PIF‟s

This will eliminate inaccurate bias values as a result of spectraldissimilarities between features such as vegetation

Vegetative spectral characteristics change dramatically throughtime due to phenology.

The mean difference of the PIF‟s, (whichremain spectrally constant through time)

from master to slave image is about -62Brightness Values. This bias value can besubtracted from the entire slave sceneleaving approximately the sameradiometric scale as the master image.



Before

radiometriccorrection

After

Notedifferencesin tone



Statistical Results

To test if the pixels of the slave and master image aresimilar in brightness values, a 1 sample t-test wasperformed.

The difference between the overlapping area of a) the hazeadjusted master and b) the normalized slave scene wascomputed.

5,000 pixels were randomly sampled from the differencedimage.

If the radiometric normalization corrected dissimilarities inBV‟s between the images, then the mean difference of theoverlapping area should equal 0

Ho: μdiff = 0 Ha: μdiff ≠ 0 t-statistic -10.757 << 1.95 (95% significance) then Ho is

accepted, thus band 1 of the master and slave scenes arestatistically similar.



Vendors Provide Some Corrections

Radiometrically corrected and calibratedspectral data in physical units at fullinstrument resolution as required

Radiometrically corrected data that have

also been spatially resampled Radiometrically corrected data with

temporal compositing

Radiometrically corrected data withconversion to specific physical orbiophysical parameters such as soilmoisture, leaf area index, absorbed

photosynthetically active radiation, etc.



Summary

Radiometric corrections are needed to correct forerrors, such as detector anomalies, sometimesfound in image data

It is possible to correct for variable image-

illumination conditions; i.e., normalize imagery toa constant solar position (e.g., overhead)

Conversions of raw DN‟s to physical units isnecessary for scientific investigations

Normalizing of multiple images to a selectedmaster scene for a study area may be done usingpseudo-invariant ground features

radiometric correction.ppt

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