Remote Sensing Classification Methods

1. Remote Sensing ClassificationMethods

2. Introduction to Remote Sensing Example Applications and Principles • What is Classification? • Explore and Classify an Image with MultiSpec Questions…

3. Definitions Lillesand and Kiefer (1994) – “The science and art of obtaining information …acquired by a device that is not in contact with the object…” CCRS Glossary – “A group of techniques for collecting image or other forms of data … from measurements made at a distance from the object, and the processing and analysis of the data.”

4. Examples of Remotely Sensed Data • Weather • Ocean Properties • Physical Geography • Major Disturbance Events / Hazards • Cultural Features – urban mapping • Many other examples – thematic info

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10. Decision From Lillesand and Kiefer (1994)

14. O3 absorption at 0.2 um

15. H2O absorption at 1.4, 1.6, and 1.9 um

16. CO2 absorption at 2.0 um

17. CH4 absorption at 2.2 – 2.5 um

18. Human eye

19. E(l)i – [ E(l)a + E(l)t ] = Er(l) Incident Energy Reflected Energy

20. Images vs. Maps

21. What are the characteristics of a thematic map? • Abstraction • Finite number of specified (discrete) classes • Scale (coarse) • Sharp boundaries – cartographic tradition, vector representation

22. Finite number of specified classes Generalize to a hierarchy of classes • Urban • Residential • Commercial • Industrial • Agricultural • Cropland • Pasture • Orchards • Etc…

23. Scale • Scale of map determines the scale (and detail) of classes 100 Pixel Size(meters) 10 1 II III I Level of Detail(Coarse to Fine)

24. Scale of imagery determines detail of what one sees Forest Community types Individual tree species Branches, leaf City Neighborhood Block House (roof) & Garden Scale

25. Sharp boundaries • Sharp • Political boundary • Geological contact (unconformity) • Fuzzy • Vegetation community • Wetland to dry area In remote sensing: One class per pixel, and thus sharp boundaries

26. How to go from remote sensing data to classes? • Humans – strong pattern recognition ability • Spatial • Shape • Size • Context • Spectral • Relative brightness • Color • Remote Sensing • Usually purely spectral: each pixel classified independently

27. Snow Vs. Clouds

28. Snow Vs. Clouds Clouds scatterat all wavelengths Snow absorbs at >1.4 mm c

31. Remote Sensing Pixel Data B1 B2 B3 . . . Bn z z z z pixel 1,1 = z z z x x z z x x z x x z Band 2 x z x y y y y y y y y y Band 1 y = forest, x = agricultural field, z = natural grassland

32. Remote Sensing Pixel Data Decision Boundary B1 B2 B3 . . . Bn z z z z pixel 1,1 = z z z x x z z x x z x x z Band 2 x z x y y y y y y y y y Band 1 y = forest, x = agricultural field, z = natural grassland

33. Classification Approaches • Supervised • Analyst identifies representative training sites for each informational class • Algorithm generates decision boundaries • Unsupervised • Algorithm identifies clusters in data • Analyst labels clusters

34. Supervised Classification Analyst identifies training areas for each informational class Algorithm identifies signatures (mean, variance, covariance, etc) Classify all pixels Informational Class Map

35. Unsupervised Classification Algorithm clusters data – find inherent classes Classify all pixels based on clusters Spectral class map Analyst labels clusters (May involve grouping of clusters) Informational class map

36. x x x x x y Band 2 y y y y Band 1 Labeling of classes • Bispectral plots • “Spectral curves” • Classification map

37. Spectral Classes Vs. Informational Classes Ideal Spectral classes Informational Classes 1 2 A B Concrete Reality 1 2 A B C Or 12 3 4 A B C Fresh blacktop Old blacktop

38. Unsupervised Classification Methods • Histogram peak/valley finding • ISODATA – moving means

39. Histogram peak/valley finding Histogram of values in the image How many classes present? Frequency DN Value of Band x Assumptions: More pure pixels than mixed pixels Classes are distinctive

40. Histogram peak/valley finding Histogram of values in the image How many classes present? Frequency DN Value of Band x How do we identify significant peaks?

41. ISODATA Iterative Self Organizing Data Analysis Technique • More robust • User specifies • Iteration • Maximum # • Minimum change between iterations • Clusters • Maximum # (less usually found) • Starting locations (along diagonal, or 1st PC, or arbitrary) • Splitting and merging parameters • Minimum # of pixels per cluster (e.g. 0.01% of the data, then merge) • Maximum spread (e.g. if standard deviation large, then split)

42. ISODATA Classification: 1Initial classification from arbitrary cluster centers Minimum distance to means classification rule x x x Total distance = 77 Band 2 x x + x x x x x Arbitrary cluster centers + Band 1

43. ISODATA Classification: 2Initial classification from arbitrary cluster centers Minimum distance to means classification rule x x x + Total distance = 54 Band 2 x x x x x x + x Adjusted cluster centers Band 1

44. ISODATA Classification: 3Initial classification from arbitrary cluster centers Minimum distance to means classification rule x x + x Total distance = 26 Band 2 x x x x x + x x Band 1

45. ISODATA Classification: 4Initial classification from arbitrary cluster centers Minimum distance to means classification rule x x + x Total distance = 18 Band 2 x x x x + x x x Band 1

46. ISODATA Classification: 4Initial classification from arbitrary cluster centers Minimum distance to means classification rule x x + x Total distance = 18 Band 2 x x x x + x x No changes in pixels between classes, therefore stop x Band 1

47. MultiSpec RS Software • Research and education • Long period development and refinement • Freely available • Supported by tutorials http://cobweb.ecn.purdue.edu/~biehl/MultiSpec/