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Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image enhancement IIPowerPoint Presentation

Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image enhancement II

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### Maa-57.2040 Kaukokartoituksen yleiskurssiGeneral Remote SensingImage enhancement II

### Examples of processing chains

Autumn 2007

Markus Törmä

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 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

- 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 = NIR / PUN
- values: 0 - inf

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

IPVI: Infrared Percentage Vegetation Index:

- IPVI = NIR/(NIR+PUN)
- values: 0 - +1

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

- 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 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 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

- NDMI = ( NIR - MIR ) / ( NIR + MIR )
- E.g. ( ETM4 - ETM5 ) / ( ETM4 + ETM5 )

Normalized Difference Snow Index

- NDSI = ( GREEN – MIR ) / GREEN + MIR )
- E.g. ( ETM2 – ETM5 ) / ( ETM2 + ETM5 )

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

- Compute vegetation indexes for images taken at different times
- Simple way to characterize changes in vegetation

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 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 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)

- 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: brightnessG: greennessB: moisture

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 -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-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

- 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 Analysis

- Landsat ETM:6 channel, 6-dimensional space
- Usually 3 first principal component as computed

PCA example 1

- Porvoo: Landsat ETM 743 and PCA 123
- Principal component images have been computed from all ETM-channels

PCA example 1

- Landsat ETM 743 and PCA 1

PCA example 1

- Landsat ETM 743 and PCA 2

PCA example 1

- Landsat ETM 743 and PCA 3

PCA example 1

- Landsat ETM 743 and PCA 4

PCA example 1

- Landsat ETM 743 and PCA 5

PCA example 1

- Landsat ETM 743 and PCA 6

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

- 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

- 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

- Sköldvik Landsat ETM 342 and PAN

Spatial resolution enhancement

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

Spatial resolution enhancement

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

Finnish IMAGE2000 for Corine 2000 Land Cover Classification

NAPS/AKO at Finnish Environment Institute

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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