Maa 57 2040 kaukokartoituksen yleiskurssi general remote sensing image enhancement ii
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Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image enhancement II. Autumn 2007 Markus Törmä [email protected] Image indexes. Idea is to combine different channels from multispectral image so that desired feature is enhanced ratio, difference or combination of these

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Maa 57 2040 kaukokartoituksen yleiskurssi general remote sensing image enhancement ii

Maa-57.2040 Kaukokartoituksen yleiskurssiGeneral Remote SensingImage enhancement II

Autumn 2007

Markus Törmä

[email protected]


Image indexes
Image indexes

  • Idea is to combine different channels from multispectral image so that desired feature is enhanced

    • ratio, difference or combination of these

    • larger value, feature is more present

  • It is useful to know spectral characteristics of different material when developing index

  • Vegetation indexes most important group



Vegetation index
Vegetation index

  • Vegetation index is a number that is

    • generated by some combination of remote sensing bands and

    • may have some relationship to the amount of vegetation in a given image pixel

  • Vegetation indices are generally based on empirical evidence and not basic biology, chemistry or physics

  • A FAQ on Vegetation in Remote Sensing http://hyperdaac.webthing.com/html/rsvegfaq.txt


Basic assumptions made by the vegetation indices
Basic assumptions made by the vegetation indices

  • Some algebraic combination of remotely-sensed spectral bands can tell you something useful about vegetation

    • There is fairly good empirical evidence that they can

  • All bare soil in an image will form a line in spectral space

    • This line is considered to be the line of zero vegetation

  • Isovegetation lines: lines of equal vegetation

    • All isovegetation lines converge at a single point

    • Measure the slope of the line between the point of convergence and the red-NIR point of the pixel

    • E.g. NDVI, SAVI, and RVI

    • All isovegetation lines remain parallel to soil line

    • Measure the perpendicular distance from the soil line to the red-NIR point of the pixel

    • E.g. PVI, WDVI, and DVI


Rvi ratio vegetation index
RVI (ratio vegetation index)

  • RVI = NIR / PUN

  • values: 0 - inf


Ndvi normalized difference vegetation index
NDVI: Normalized Difference Vegetation Index

  • NDVI = (NIR-PUN)/(NIR+PUN)

  • values: -1 - +1

  • most used and well-known

  • water: low (negative) values

  • forest 0.5-0.8

  • open land 0.5-0.6


April 19

Clouds: grey

Areas with chlorophyll: white

Snow in Lapland: dark grey

Water: black

NDVI



Ipvi infrared percentage vegetation index
IPVI: Infrared Percentage Vegetation Index:

  • IPVI = NIR/(NIR+PUN)

  • values: 0 - +1


Some more
Some more

  • Difference Vegetation Index (DVI):

    DVI = NIR - PUN

    values: -max(PUN) - max(NIR)

  • Transformed Vegetation Index (TVI):

    TVI = ((NIR-PUN)/(NIR+PUN)+0.5)0.5 x 100


Soil line
Soil line

  • Line in spectral space

    • describes the variation of bare soil in the image

  • Line can be found by locating two or more patches of bare soil in the image having different reflectivities and finding the best fit line in spectral space


Vegetation index1
Vegetation index

  • Some vegetation indices use information about soil line

  • Perpendicular Vegetation Index

    PVI = sin(a)NIR-cos(a)red

    • a is the angle between the soil line and the NIR axis

  • Weighted Difference Vegetation Index

    WDVI = NIR-g*red

    • g is the slope of the soil line


Vegetation index2
Vegetation index

  • Some vegetation indices try to minimize soil noise

    • All of the vegetation indices assume that there is a single soil line

    • However, it is often the case that there are soils with different red-NIR slopes in a single image

    • Changes in soil moisture change index value

    • Problem of soil noise is most acute when vegetation cover is low

  • Soil Adjusted Vegetation Index

    SAVI = (( NIR-red )/(NIR+red+L))(1+L)

    • L is a correction factor which ranges from 0 (high vegetation cover) to 1 (low cover)


Normalized difference moisture index
Normalized Difference Moisture Index

  • NDMI = ( NIR - MIR ) / ( NIR + MIR )

    • E.g. ( ETM4 - ETM5 ) / ( ETM4 + ETM5 )


Normalized difference snow index
Normalized Difference Snow Index

  • NDSI = ( GREEN – MIR ) / GREEN + MIR )

    • E.g. ( ETM2 – ETM5 ) / ( ETM2 + ETM5 )


Spectral indices disadvantages
Spectral Indices Disadvantages

  • Not physically-based

    • Empirical Relations

    • Correlation not Causality

    • NDVI vs. Tourism in Italy

  • Only small amount of spectral information used

    • Rarely simple relationship between variable and index


Difference in vegetation indexes difference in vegetation
Difference in vegetation indexes:difference in vegetation

  • Compute vegetation indexes for images taken at different times

    • Simple way to characterize changes in vegetation


Tasseled cap transform
Tasseled cap transform

  • Linear transform for multispectral images

  • Multispectral image is tarnsformed to images describing some scene property

    • brightness

    • greenness

    • moisture

    • haze

  • Originally developed for Landsat MSS, then TM, ETM and other instruments


Tasseled cap transform1
Tasseled cap transform

  • Kauth and Thomas noticed that growing cycle of crop

    • started from bare soil

    • then to green vegetation and

    • then to crop maturation with crops turning yellow

http://www.cnr.berkeley.edu/~gong/textbook/chapter6/html/sect65.htm


Tasseled cap transform2
Tasseled cap transform

  • They developed linear transformation to characterize that

  • Landsat MSS:

    • Redness (soil)

    • Greenness (vegetation)

    • Yellowness

    • Noise

http://www.cnr.berkeley.edu/~gong/textbook/chapter6/html/sect65.htm


Tasseled cap landsat 7 etm
Tasseled Cap (Landsat-7 ETM)

  • ETM-image should be converted to radiances

  • Brightness = 0.3561 * Ch1 + 0.3972 * Ch2 + 0.3904 * Ch3 + 0.6966 * Ch4 + 0.2286 * Ch5 + 0.1595 * Ch7

    - Corresponds to soil reflectance

  • Greenness = -0.3344 * Ch1 - 0.3544 * Ch2 - 0.4556 * Ch3 + 0.6966 * Ch4 - 0.0242 * Ch5 - 0.2630 * Ch7

    - Amount of vegetation

  • Moisture= 0.2626 * Ch1 + 0.2141 * Ch2 + 0.0926 * Ch3 + 0.0656 * Ch4 - 0.7629 * Ch5 - 0.5388 * Ch7

    - Soil and vegetation moisture





R brightness g greenness b moisture
R: brightnessG: greennessB: moisture


Karhunen l we transform
Karhunen -Löwe transform

  • Aim is to decrease number of channels and preserve information

  • Idea: remove correlations between channels

    • same information in different channels

  • E.g.: TM-image, 6 channels  transformed image, 3 channels


Karhunen l we transform1
Karhunen -Löwe transform

  • y = A * x

  • x original pixels

  • y transformed pixels

  • A transformation matrix

  • Transformation matrix compresses information to less number of channels than originally


Karhunen l we muunnos
Karhunen-Löwe muunnos

  • Different transformation matrices:

    • Principal component analysis / transformation: variance of data is maximized

    • Canonical correlation: maximize class separability

  • Based on turning of coordinate system according to largest variance


Principal component analysis
Principal Component Analysis

  • PCA: Principal Component Analysis

  • Mean vector of data

  • Covariance matrix of data

    • describes the variance of data according to different coordinate axis

  • Hypothesis:

    • large variance  much information


Principal component analysis1
Principal Component Analysis

1. PC

Channel 2

Channel 1


Principal component analysis2
Principal Component Analysis

  • Landsat ETM:6 channel, 6-dimensional space

  • Usually 3 first principal component as computed


Pca example 1
PCA example 1

  • Porvoo: Landsat ETM 743 and PCA 123

  • Principal component images have been computed from all ETM-channels


Pca example 11
PCA example 1

  • Landsat ETM 743 and PCA 1


Pca example 12
PCA example 1

  • Landsat ETM 743 and PCA 2


Pca example 13
PCA example 1

  • Landsat ETM 743 and PCA 3


Pca example 14
PCA example 1

  • Landsat ETM 743 and PCA 4


Pca example 15
PCA example 1

  • Landsat ETM 743 and PCA 5


Pca example 16
PCA example 1

  • Landsat ETM 743 and PCA 6


Pca example
PCA example

  • Proportion of variances of different principal component images

    • 73 %

    • 19 %

    • 3 %

    • 0.7 %

    • 0.3 %

    • 0.2 %

  • Three first: about 99% information


Decorrelation strecth
Decorrelation strecth

  • Image enhancement method

  • Make PCA-images

  • PCA-images are scaled (streched) so that their variance is equal to variance of first PCA-image

  • Make inverse PCA, i.e. return to original image-space


Data fusion spatial resolution enhancement
Data fusion: Spatial resolution enhancement

  • Generally:

    • Good spatial resolution  bad spectral or radiometric resolution

    • Bad spatial resolution  good spectral or radiometric resolution

  • For example:

    • Spot-5 PAN: 5m, 0.48 - 0.71 µm

    • Spot-5 XS: 10m, Green: 0.50 – 0.59 µm, red: 0.61 – 0.68 µm, NIR: 0.78 – 0.89 µm, 20m, SWIR: 1.58 – 1.75 µm


Spatial resolution enhancement
Spatial resolution enhancement

  • Sköldvik Landsat ETM 342 and PAN


Spatial resolution enhancement1
Spatial resolution enhancement

  • Sköldvik Landsat ETM 342 and PAN- ja XS-average image


Spatial resolution enhancement2
Spatial resolution enhancement

  • Sköldvik Landsat ETM 342 and data fusion by principal component method


Examples of processing chains

Examples of processing chains

Finnish IMAGE2000 for Corine 2000 Land Cover Classification

NAPS/AKO at Finnish Environment Institute


Processing of finnish image2000
Processing of Finnish IMAGE2000

  • 36 Landsat ETM-images

  • Orthocorrection by Metria Sweden

    • 25 m pixel size

    • Average RMSE error of test points 12.9 m

  • Cloud and shadow masking by visual interpretation

  • Atmospheric correction using VTT-SMAC

  • Topographic correction in Northern Finland

  • Mosaicking according to vegetation zones


EO-data distributer (FMI, K-Sat, …)

Data

Data

Archieving

FTP-box

Product calculation & data delivery (SYKE)

End users

citizens

runoff forecasts

forest industry

climate change research

watershed research

water protection

  • Data delivery

  • WWW

  • Map user interface

  • numerical data

tourism

hydropower industry

Algorithm & Cloud masking

End-product

Production line for EO data (MODIS, NOAA AVHRR)

Automated processing system (SYKE)

  • Image processing:

  • Unpacking

  • Radiometric calibration and atmospheric correction

  • Geometric correction

Data in usable form for the algorithms


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