Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image enhancement II
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Transcript of Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image enhancement II
Maa-57.2040 Kaukokartoituksen yleiskurssi
General Remote Sensing
Image enhancement II
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
Spectra taken from ASTER Spectral Library
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 vegetation1. 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
2. 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
NDVI
• April 19• Clouds: grey• Areas with
chlorophyll: white• Snow in Lapland: dark
grey• Water: black
NDVI
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
Brightness
Greenness
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
Channel 2
Channel 1
1. PC
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 1. 73 %
2. 19 %
3. 3 %
4. 0.7 %
5. 0.3 %
6. 0.2 %
• Three first: about 99% information
Decorrelation strecth
• Image enhancement method
1. Make PCA-images
2. PCA-images are scaled (streched) so that their variance is equal to variance of first PCA-image
3. 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
Examples of processing chains
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
Production line for EO data (MODIS, NOAA AVHRR)
Algorithm & Cloud masking End-product
Data delivery-WWW
-Map user interface
-numerical data
Product calculation & data delivery (SYKE)
water protectionwatershed researchclimate change research
forest industry
tourism
citizens
hydropower industry
runoff forecasts
End users
Dat
a
Image processing:
1. Unpacking
2. Radiometric calibration and atmospheric correction
3. Geometric correction
Data in usable form for the algorithms
Automated processing system (SYKE)
EO-data distributer (FMI, K-Sat, …) D
ata
FTP-box Archieving