Digital Image Classification- BY aleemuddin Univ-Jamia Millia Islamia- New Delhi-INDIA

1. iirs DIGITAL IMAGE CLASSIFICATION POONAM S. TIWARI Photogrammetry and Remote Sensing Division

14. One-dimensional feature space Input layer Distinction between classes No distinction between classes

15. Feature Space Multi-dimensional Feature vectors

16. Two/three dimensional graph or scattered diagram formation of clusters of points representing DN values in two/three spectral bands. Each cluster of points corresponds to a certain cover type on ground Low frequency High frequency Feature space (s cattergram )

17. Distances and clusters in feature space Euclidian distance Cluster

18. Euclidean Spectral distance is distance in n - dimensional spectral space. It is a number that allows two measurement vectors to be compared for similarity. The spectral distance between two pixels can be calculated as follows: Spectral Distance Where: D = spectral distance n = number of bands (dimensions) i = a particular band d i = data file value of pixel d in band i e i = data file value of pixel e in band i This is the equation for Euclidean distance—in two dimensions (when n = 2), it can be simplified to the Pythagorean Theorem ( c 2 = a 2 + b 2 ), or in this case: D 2 = ( d i - e i ) 2 + ( d j - e j ) 2

19. Image classification process Validation of the result Definition of the clusters in the feature space Selection of the image data 1 2 3 4 5

34. Signature Seperability……… 3. Transformed Divergence The scale of the divergence values can range from 0 to 2,000. As a general rule, if the result is greater than 1,900, then the classes can be separated. Between 1,700 and 1,900, the separation is fairly good. Below 1,700, the separation is poor (Jensen, 1996).

39. 0 255 0 255 Band 1 Means and Standard Deviations 0 255 0 255 Band 2 Band 1 Feature Space Partitioning - Box classifier Partitioned Feature Space Band 2

40. Class “unknown”

41. Points a and b are pixels in the image to be classified. Pixel a has a brightness value of 40 in band 4 and 40 in band 5. Pixel b has a brightness value of 10 in band 4 and 40 in band 5. The boxes represent the parallelepiped decision rule associated with a ±1s classification. The vectors ( arrows ) represent the distance from a and b to the mean of all classes in a minimum distance to means classification algorithm.

46. MINIMUM DISTANCE TO MEANS Decision rule: Priority to the shortest distance to the class mean 0 31 63 95 127 159 191 223 255 300 200 100 0 Histogram of training set

47. Feature Space Partitioning - Minimum Distance to Mean Classifier 0 255 0 255 Band 2 Band 1 255 0 255 Band 2 Band 1 0 0 255 Band 2 Band 1 Mean vectors 0 255 "Unknown" Threshold Distance

58. Original brightness values of pixels 1, 2, and 3 as measured in Bands 4 and 5 of the hypothetical remote sensed data.

59. The distance ( D ) in 2-dimensional spectral space between pixel 1 (cluster 1) and pixel 2 (potential cluster 2) in the first iteration is computed and tested against the value of R =15, the minimum acceptable radius. In this case, D does not exceed R . Therefore, we merge clusters 1 and 2 as shown in the next illustration.

60. Pixels 1 and 2 now represent cluster #1. Note that the location of cluster 1 has migrated from 10,10 to 15,15 after the first iteration. Now, pixel 3 distance (D=15.81) is computed to see if it is greater than the minimum threshold, R=15. It is, so pixel location 3 becomes cluster #2. This process continues until all 20 clusters are identified. Then the 20 clusters are evaluated using a distance measure, C (not shown), to merge the clusters that are closest to one another.

61. How clusters migrate during the several iterations of a clustering algorithm. The final ending point represents the mean vector that would be used in phase 2 of the clustering process when the minimum distance classification is performed.

63. Pass 2: Assignment of Pixels to One of the C max Clusters Using Minimum Distance Classification Logic The final cluster mean data vectors are used in a minimum distance to means classification algorithm to classify all the pixels in the image into one of the C max clusters.

64. ISODATA Clustering The Iterative Self-Organizing Data Analysis Technique (ISODATA) represents a comprehensive set of heuristic (rule of thumb) procedures that have been incorporated into an iterative classification algorithm. The ISODATA algorithm is a modification of the k -means clustering algorithm, which includes a) merging clusters if their separation distance in multispectral feature space is below a user-specified threshold and b) rules for splitting a single cluster into two clusters. ISODATA is iterative because it makes a large number of passes through the remote sensing dataset until specified results are obtained, instead of just two passes. ISODATA does not allocate its initial mean vectors based on the analysis of pixels rather, an initial arbitrary assignment of all Cmax clusters takes place along an n -dimensional vector that runs between very specific points in feature space.

72. There are a number of issues relevant to the generation and assessment of errors in a classification. These include: • the nature of the classification ; • Sample design and • assessment sample size .

85. Thank You