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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 Markus.Torma@tkk.fi. 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

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  1. Maa-57.2040 Kaukokartoituksen yleiskurssiGeneral Remote SensingImage enhancement II Autumn 2007 Markus Törmä Markus.Torma@tkk.fi

  2. 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

  3. Spectra taken from ASTER Spectral Library

  4. 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

  5. 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

  6. RVI (ratio vegetation index) • RVI = NIR / PUN • values: 0 - inf

  7. 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

  8. April 19 Clouds: grey Areas with chlorophyll: white Snow in Lapland: dark grey Water: black NDVI

  9. NDVI

  10. IPVI: Infrared Percentage Vegetation Index: • IPVI = NIR/(NIR+PUN) • values: 0 - +1

  11. 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

  12. 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

  13. 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

  14. 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)

  15. Normalized Difference Moisture Index • NDMI = ( NIR - MIR ) / ( NIR + MIR ) • E.g. ( ETM4 - ETM5 ) / ( ETM4 + ETM5 )

  16. Normalized Difference Snow Index • NDSI = ( GREEN – MIR ) / GREEN + MIR ) • E.g. ( ETM2 – ETM5 ) / ( ETM2 + ETM5 )

  17. 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

  18. Difference in vegetation indexes:difference in vegetation • Compute vegetation indexes for images taken at different times • Simple way to characterize changes in vegetation

  19. 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

  20. 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

  21. 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

  22. 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

  23. Brightness

  24. Greenness

  25. Moisture

  26. R: brightnessG: greennessB: moisture

  27. 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

  28. 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

  29. 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

  30. 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

  31. Principal Component Analysis 1. PC Channel 2 Channel 1

  32. Principal Component Analysis • Landsat ETM:6 channel, 6-dimensional space • Usually 3 first principal component as computed

  33. PCA example 1 • Porvoo: Landsat ETM 743 and PCA 123 • Principal component images have been computed from all ETM-channels

  34. PCA example 1 • Landsat ETM 743 and PCA 1

  35. PCA example 1 • Landsat ETM 743 and PCA 2

  36. PCA example 1 • Landsat ETM 743 and PCA 3

  37. PCA example 1 • Landsat ETM 743 and PCA 4

  38. PCA example 1 • Landsat ETM 743 and PCA 5

  39. PCA example 1 • Landsat ETM 743 and PCA 6

  40. PCA example • Proportion of variances of different principal component images • 73 % • 19 % • 3 % • 0.7 % • 0.3 % • 0.2 % • Three first: about 99% information

  41. 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

  42. 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

  43. Spatial resolution enhancement • Sköldvik Landsat ETM 342 and PAN

  44. Spatial resolution enhancement • Sköldvik Landsat ETM 342 and PAN- ja XS-average image

  45. Spatial resolution enhancement • Sköldvik Landsat ETM 342 and data fusion by principal component method

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