Image Classification: Introduction

1. Image Classification: Introduction Lecture Notes 7 prepared by R. Lathrop 11/99 updated 3/04 Readings: ERDAS Field Guide 5th Ed. CH. 6:217-234

2. Where in the World?

3. Learning objectives • Remote sensing science concepts • Rationale and theory behind image classification • Image spectral vs. information classes • Unsupervised (ISODATA) algorithm • Clusterbusting • Math Concepts • Calculating mage distance • Skills • Applying ISODATA algorithm

4. Image Classification One of the major applications of remotely sensed imagery is to provide information on the amount and spatial distribution of various types of land use and land cover land cover - the biophysical material covering the land surface land use - the use to which land is put by humans Move toward more automated procedures using digital image processing to map land use-land cover

5. Computer-assisted classification of remotely sensed images • Automatically categorize all pixels in an image into land cover classes or themes Convert image data into information • Normally uses multi-spectral data and spectral pattern recognition techniques as compared to spatial or temporal pattern recognition to aid in identification

6. Objective: Image to Thematic Map

7. Remotely Sensed Image Classification • 1st step: identify classification scheme to be applied • Hierarchical approach of increasing specificity Level I: most general Level II: more specific • Level of classification depends on the spatial, spectral, temporal and radiometric resolution of the image data

8. National Land Cover Dataset Classification system: 21 classes • Water • 11 Open Water12 Perennial Ice/Snow • Developed • 21 Low Intensity Residential22 High Intensity Residential23 Commercial/Industrial/Transportation • Barren • 31 Bare Rock/Sand/Clay32 Quarries/Strip Mines/Gravel Pits33 Transitional • Forested Upland • 41 Deciduous Forest42 Evergreen Forest43 Mixed Forest • Shrubland • 51 Shrubland • Non-Natural Woody • 61 Orchards/Vineyards/Other • Herbaceous Upland Natural/Semi-natural Vegetation • 71 Grasslands/Herbaceous • Herbaceous Planted/Cultivated • 81 Pasture/Hay82 Row Crops83 Small Grains84 Fallow85 Urban/Recreational Grasses • Wetlands • 91 Woody Wetlands92 Emergent Herbaceous Wetlands http://landcover.usgs.gov/prodescription.asp

9. Feature Space Image • Visualization of 2 bands of image data simultaneously through a 2 band scatterplot - the graph of the data file values of one band of data against the values of another band • Feature space - abstract space that is defined by spectral units

10. Each dot represents a pixel; the warmer the colors, the higher the frequency of pixels in that portion of the feature space

11. Spectral Pattern Recognition • Numerical process whereby elements of multi-spectral image data sets are categorized into a limited number of spectrally separable, discrete classes: • 1) show (train) the computer the multiple spectral band data associated with land cover type of interest • 2) the computer decides, using some form of classification decision rule, which land cover type each pixel most looks like

12. Classification can be thought of as trying to relate spectral classes or locations in the feature space with the appropriate information class

13. Spectral vs. Information Class • Spectral class - group (cluster) of spectrally "like" pixels • Information class - land use/land cover class of interest • May take many spectral classes to describe one information class. One spectral class may represent more than 1 information class.

14. Spectral vs. Information Classes: May take many spectral classes to describe one information class. One spectral class may represent more than 1 information class. Spectral ClassInformation Class Sunlit conifer Upland Conifer Hillside shadowed conifers Upland Deciduous Deciduous broadleaf Lowland Deciduous

15. Spectral Classes: pixels of one land cover type tend to cluster together Veg 1 NIR reflectance Veg3 Soil 3 Veg 2 Soil 2 Soil 1 Water 2 Water 1 Red reflectance Adapted from J.A. Richards, 1986

16. Spectral vs. Information Classes Soil Information class Veg 1 NIR reflectance Veg3 Soil 3 Veg 2 Soil 2 Soil 1 Water 2 Water 1 Red reflectance Adapted from J.A. Richards, 1986

17. NIR reflectance Red reflectance Spectral & information classes do not always have a 1-to-1 match Same spectral class may belong to more than one information class Veg 1 Developed 3 Veg3 Soil 3 Veg 2 Developed 2 Soil 2 Developed 1 Soil 1 Water 2 Water 1 Adapted from J.A. Richards, 1986

18. Classification Process • 1) Training/Clustering Stage - the process of defining criteria by which spectral patterns are recognized, developing a numerical description for each spectral class • 2) Classification Stage - each pixel in the image data set is categorized into the spectral class it most closely resembles based on a mathematical decision rule • 3) Output Stage - results are presented in a variety of forms (tables, graphics, etc.)

19. Multispectral classification Multispectral image classification using spectral pattern recognition often relies on measuring the “likelihood” that a pixel belongs to one class vs. another. This likelihood generally relies on some measure of distance between a pixel and the various spectral classes clusters. For example, if a pixel is “closest” to Spectral Class 1 vs. Spectral Class2, then the pixel is classified into spectral Class 1. Spectral distance can be measured in several ways: - as simple euclidean distance in multispectral space - as a statistical distance or probability

20. Spectral distance • Spectral distance - the Euclidean distance in n-dimensional spectral space • D = SQRT[(sum (dk - ek)2] where dk = BV of pixel d in band k where ek = BV of pixel e in band k • the equation is summed across k = 1 to n bands

21. What is the spectral distance between Pixel A and Cluster 1? Pixel A Y 92, 153 Cluster 1 180, 85 X

22. Spectral Distance example Distance between [x1,y1] & [x2, y2] [180, 85] & [92, 153] D = SQRT[(sum (dk - ek)2] D = SQRT[(180-92)2 + (85-153)2] = SQRT[(88)2 + (-68)2] = SQRT[7744 + 4624] = SQRT[12,368] = 111.2

23. Y X Spectral Distance example 92, 153 Yd = 85-153 180, 85 Xd = 180 -92

24. Supervised vs. Unsupervised Approaches • Supervised - image analyst "supervises" the selection of spectral classes that represent patterns or land cover features that the analyst can recognize Prior Decision • Unsupervised - statistical "clustering" algorithms used to select spectral classes inherent to the data, more computer-automated Posterior Decision

25. NIR Red Supervised vs. Unsupervised Supervised Prior Decision: from Information classes in the Image to Spectral Classes in Feature Space Unsupervised Posterior Decision: from Spectral Classes in Feature Space to Information Classes in the Image

26. Supervised vs. Unsupervised Run clustering algorithm Select Training fields Edit/evaluate signatures Identify classes Edit/evaluate signatures Classify image Evaluate classification Evaluate classification

27. ISODATA (Iterative Self-Organizing Data Analysis Technique) Clustering Algorithm • User specified Input (as implemented in ERDAS) • maximum number of clusters • Initial allocation of cluster center locations • maximum % of pixels whose class values are allowed to be unchanged between iterations. • maximum number of iterations • Other possible constraints (not implemented in ERDAS) • minimum number of members in a cluster, if fall below threshold then that cluster eliminated • maximum standard deviation: if the std dev exceeds the threshold then that cluster is split into two • minimum distance between cluster means

28. NIR Red Initial Cluster Allocation • clusters allocated along the mean n-dimensional vector • spaced according to std dev distance away from central mean

29. Algorithm Operation • each pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance ________________________________ \/(DNb1i-DNb1m)2 + ... + (DNbxi - DNbxm)2)) and a new cluster center is computed by averaging the locations of all the pixels assigned to that cluster.

30. NIR NIR Red Red • ISODATA: multiple iterations from initial allocation to final assignment • The algorithm will stop either when the # iteration threshold is reached Or the max % of unchanged pixel threshold is reached Initial clusters Final clusters Adapted from Jensen 2nd ed, 1996

31. Example: ISODATA clustering Initial clusters: 1 (10,10), 2 (20,20) 3 (30,20) Unclassified Pixel (30,10) 1. Assigning unclassified pixels to cluster means 2. Calculating new cluster means - Cluster 1 & 2 unchanged - Cluster 3 migrates to Band 4mean, Band 5mean = 30, 15

32. Example of Naturally Clustered Data Green Vegetation NIR Senesced Vegetation Red Adapted from Swain

33. Initial cluster centers After 1st iteration NIR NIR NIR NIR Red Red Red Red x x x x After 2nd iteration Final cluster centers x x x x

34. Final Cluster Centers Green Vegetation X NIR X Senesced Vegetation Red Adapted from Swain

35. In spectral feature space, generally no distinct, isolated clusters, rather a continuous gradient. Classification can be thought of as trying to subdivide the feature space into appropriate spectral regions

36. Algorithm Operation • The objective is to minimize the Sum of Squared Errors (SSE) across the entire set of clusters and the original pixel data • The Sum of Squared Errors (SSE): is the cumulative squared difference (i.e., spectral distance across all the various bands) of each pixel from its assigned cluster center • An optimal arrangement has been reached that minimizes the distance between every pixel and a cluster center (based on that number of clusters)

37. Initial clusters (2,1) & (4,2) 1st iteration • X Y DistA DistB SSE • 2 1.41 3.00 2.0 • 3 2.00 2.24 4.0 • 2 4 3.00 2.83 8.0 • 4 3.16 2.24 5.0 • 3 2.83 1.00 1.0 • 4 4 3.60 2.00 4.0 • 6 5.38 4.00 16.0 • 7 6.71 5.10 26.0 • 6 5 5.66 3.60 13.0S = 79 HWK Example: SSE calculation New cluster centers (1.5, 2.5) (4, 4.6)

38. Post-clustering Assignment • The analyst must then assign each spectral cluster to an information class based on available ancillary information (e.g., field reference data, maps, aerial photos, analyst experience) Posterior Decision process • If one and only one information class can not be unequivocally assigned to a cluster then assign the cluster to a “mixed” class

39. Post Clustering Assignment: what information class can be assigned to each spectral cluster? NIR Red 21:low intensity residential 71:grass 33:transitional 31:Bare rock 41:decidous forest 32: Quarries 84:fallow 91:wetlands 22: high intensity residential 42 evergreen forest 23: Commercial 11:water Adapted from Jensen 2nd ed, 1996

40. ISODATA Clustering: Pros • clustering not geographically biased to any particular portion of the image • highly successful at finding inherent spectral clusters • results similar to a minimum-distance-to-means classifier

41. ISODATA Clustering: Cons • analyst doesn’t know a priori number of spectral classes • number of iterations needed, can be time consuming • does not account for pixel spatial homogeneity • Insensitive to variance/covariance

42. Cluster-busting • technique to iteratively “bust up” spectrally “mixed” classes • separate “good” vs. “bad” classified pixels into a binary mask • mask out the “good” image, extract the “bad” image data and re-run the unsupervised process • re-evaluate new clusters, keep good, toss out “bad”, cluster bust again • create final cluster map by using a GIS overlay with a maximum dominate function

43. NIR NIR Red Red Cluster-busting: in feature space

44. Cluster-busting: in geographic space - separate “good” vs. “bad” classified pixels into a binary mask - mask out the “good” (green) image - extract the “bad”(red) image data - re-run the unsupervised process

45. Cluster-busting - re-evaluate new clusters, keep good, toss out “bad”, cluster bust again (if needed) - create final cluster map by using a GIS overlay with a maximum dominate function Overlay

46. Cluster busting Recode “good” class(es) = 0 “bad” class(es) > 1 Mask original image file

47. Cluster busting New clusters = the holes Overlay Old clusters – “bad” = swiss cheese