Readings

1. Lectures 11Analysis and Classification of Digital MSS Data 21 October 2008Today’s Lecture will be given by Dr. Tatiana Loboda, a Research Scientist in the Department of Geography

2. Readings Campbell, Chapters 12,17.9-17.10

3. Challenge in remote sensing – how does one capture the information content that is available in the different channels of the digital image Fig 1

4. Fig 2 spruce aspen lake water snow

5. Fig 3 Question: Can we train the computer to use the differences in DN values between the different channels to discriminate the different surface categories?

6. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

7. Goals for image processing To identifyand quantifyfeatures of interest with a specified degree of accuracy which allows for systematic analysis of remotely sensed data

8. Single Characteristic Multiple Categories Areas affected by fire Vegetation categories Different levels of a single characteristic Fig 4 Chlorophyll levels

9. Goals for image processing – quantify • Create maps with discrete categories -Example - estimate areas with different land cover categories in an urban region • Identify and map a specific feature of interest on different dates • Example - determine areas and rates of deforestation in tropical regions • Create a maps that represents different levels of a surface characteristic • Example - estimate net primary production in oceanic regions

10. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

11. Steps for producing a satellite-based information product • Data exploration (contrast stretching and multi-channel displays) • Are you seeing the features within the imagery that were expected? • Are their any unusual or unexpected signatures? • Do these new signatures represent important information?

12. Steps for producing a satellite-based information product • Data correction/normalization** • Geometric rectification • Radiometric normalization – conversion of DNs to a relative or absolute radiance value **Topics for GEOG472

13. Steps for producing a satellite-based information product • Initial information product generation • Product evaluation/validation • Refinement of product generation approach • Generation of final product Focus of Lecture 10 is on different approaches used to generate information products from VIS/RIR multi-channel data – step 3

14. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

15. Image Classification The process of automatically dividing all pixels within a multichannel digital remote sensing dataset into • Discrete surface-cover categories • Information themes or quantification of different levels of a specific surface characteristics

16. Regional land cover map derived from Landsat TM data Fig 5 From: http://www.fes.uwaterloo.ca/crs/geog376.f2001/ ImageAnalysis/ImageAnalysis.html#ImageProcessingSteps

17. 1: Evergreen Needleleaf Forests; 2: Evergreen Broadleaf Forests; 3: Deciduous Needleleaf Forests; 4: Deciduous Broadleaf Forests; 5: Mixed Forests; 6: Woodlands; 7: Wooded Grasslands/Shrubs; 8: Closed Bushlands or Shrublands; 9: Open Shrublands; 10: Grasses; 11: Croplands; 12: Bare; 13: Mosses and Lichens http://www.geog.umd.edu/landcover/8km-map.html Fig 6 Global Land cover map generated from AVHRR data

18. Fig 7 Ocean chlorophyll concentration derived from SeaWifs data

19. Fig 8 Land cover map generated from Shuttle Imaging Radar data

20. Fig 9 Map of areas impacted by fire generated from MODIS thermal IR data

21. Lecture Topics • Goals for Image Processing • Steps in producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

22. Fig 10 Thresholding is carried out on a single channel of data

23. Fig 11 14 15 17 15 Average = 14.75 Range = 11 - 18 16 13 18 14 16 15 13 15 17 15 11 12 Most land surfaces have a range of values, not a single value –

24. Lillesand and Kiefer Figure 7-46 Fig 12

25. Lillesand and Kiefer Figure 7-11 Fig 13

26. Water The range in digital values for these two surfaces do not overlap, so you can use a level slice to classify your image into two categories > 40 = Land < 40 = water Land Lillesand and Kiefer Figure 7-11 Fig 14

27. Thresholding/Level Slicing Limitations • When you have a variety of different types of land surfaces, using level slicing with a single channel usually only provides a small number of discrete categories

28. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

29. Two-step level slicing or thresholding Step 1 – Estimate a surface characteristic based on the relationship between that characteristic and the different bands of the MSS system Step 2 – create discrete levels of the characteristic, e.g., level slice the resultant single band channel

30. Example of 2-step level slice With AVHRR data, greenness can be estimated from the Normalized Difference Vegetation Index (NDVI) NDVI = (Near IR – Red) (Near IR + Red)

31. Fig 15 This greenness map was created by level slicing NDVI Values

32. Example of 2-step level slice With Seawifs data, chlorophyll concentration is estimated as a function of several bands Chlorophyll concentration = f (B1, B2, B3)

33. Fig 16 Chlorophyll concentration based on Seawifs algorithm

34. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

35. Fig 17

36. Fig 18

37. Fig 19 Lillesand and Kiefer Figure 7-39

38. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

39. Supervised versus Unsupervised Classification • Supervised classification – a procedure where the analyst guides or supervises the classification process by determining numerical descriptors of the land cover types of interest • Unsupervised classification – the computer is allowed to aggregate groups of pixels into clusters with similar digital values

40. Training Areas and Supervised Classification • Specified by the analyst to represent the land cover categories of interest • Used to compile a numerical “interpretation key” that describes the spectral attributes of the areas of interest • Each pixel in the scene is compared to the training areas, and then assigned to one of the categories

41. Lecture Topics • Goals for Image Processing • Steps for producing an information product from satellite data • Definition of image classification • Image slicing or thresholding • Thresholding of digital values • Thresholding of transformed values • Image categorization or classification • Supervised vs. unsupervised • Multiband Classification • Indices generated from multi-band data • Vegetation indices • Burn/fire severity indices • Global NDVI data sets • Analysis of temporal trends in vegetation indices • Decision tree classifiers

42. Multiband Classification Approaches • Minimum distance classifiers • Parallelepiped classifiers • Maximum likelihood classifiers • Decision tree classifiers – see lecture topic 9

43. Minimum Distance Classifiers Step 1 – calculate the average value for each training area in each band f f + f f f * c c c c + c * - Unclassified pixel

44. Minimum Distance Classifiers Step 2 – for each unclassified pixel, calculate the distance to the average for each training area The unclassified pixel is place in the group to which has the smallest value f f + f f f * c c c c + c

45. Minimum Distance Classifiers Fig 20 Lillesand and Kiefer Figure 7-40

46. Advantages/Disadvantages of Minimum Distance Classifiers • Advantages • Simple and computationally efficient • Disadvantages – • Does not factor in the fact that some categories have a large variance, which can lead to misclassification

47. Multiband Classification Approaches • Minimum distance classifiers • Parallelepiped classifiers • Maximum likelihood classifiers

48. Parallelepiped Classifiers Step 1 – define the range of values in each training area and use these ranges to construct an n-dimensional box (a parallelepiped) around each class f f f f f c c c c c

49. Step 2 – use the multi-dimensional ranges to create different surface categories Fig 21 Lillesand and Kiefer Figure 7-41

50. Fig 22 Regions do not have to be perfect rectangles, but can be “stepped” This reduces errors Lillesand and Kiefer Figure 7-41 This approach does not classify pixels that fall outside of the defined areas