1. Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image 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 1.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

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

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

22. 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

23. 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

24. 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

25. 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

26. Brightness

27. Greenness

28. Moisture

29. R: brightness G: greenness B: moisture

30. 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

31. 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

32. 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

33. 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

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

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

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

37. PCA example 1 Landsat ETM 743 and PCA 1

38. PCA example 1 Landsat ETM 743 and PCA 2

39. PCA example 1 Landsat ETM 743 and PCA 3

40. PCA example 1 Landsat ETM 743 and PCA 4

41. PCA example 1 Landsat ETM 743 and PCA 5

42. PCA example 1 Landsat ETM 743 and PCA 6

43. 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

45. 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

47. 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

48. Spatial resolution enhancement Sköldvik Landsat ETM 342 and PAN

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

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

51. Examples of processing chains Finnish IMAGE2000 for Corine 2000 Land Cover Classification NAPS/AKO at Finnish Environment Institute

52. 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

53. 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 protection watershed research climate change research forest industry tourism citizens hydropower industry runoff forecasts End users Data 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, …) Data FTP-box Archieving