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Digital Image Classification.pptx

  1. 1. Digital Image Classification
  2. 2. Digital Image Classification  Multispectral classification is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values.  If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to that criteria.  Multispectral classification may be performed using a variety of algorithms.  Hard classification using supervised or unsupervised approaches.  Classification using fuzzy logic, and /or  Hybrid approaches often involving use of ancillary information.
  3. 3. Multispectral Classification • Assigning each pixel in a remotely sensed image a label describing real world object.
  4. 4. What is Digital Image Classification?  Grouping of similar pixels  Separation of dissimilar ones  Assigning class label to pixels  Resulting in manageable size of classes Classification Methods  Manual  Computer Assisted  Stratified
  5. 5.  To translate continuous variability of image data into map patterns that provide meaning to the user.  To obtain insight in the data with respect to ground cover and surface characteristics.  To find anomalous patterns in the image dataset.  Cost efficient in the analyses of large datasets.  Results can be reproduced.  More objective than visual interpretation  Effective analysis of complex multi-band (spectral) interrelationships. Reasons for using Digital Image Classification
  6. 6. Dimensionality of Data  Spectral Dimensionality is determined by the number of sets of values being used in a process.  In image processing, each band of data is a set of values. An image with four bands of data is said to be four-dimensional (Jensen, 1996). Measurement Vector The measurement vector of a pixel is the set of data file values for one pixel in all n bands.
  7. 7. Mean Vector  When the measurement vectors of several pixels are analyzed, a mean vector is often calculated.  This is the vector of the means of the data file values in each band. It has n elements.
  8. 8. Image Space  Image Space (col, row)  Array of elements corresponding to reflected or emitted energy from IFOV.  Spatial arrangement of the measurements of the reflected or emitted energy.
  9. 9. Feature Space  A feature space image is simply a graph of the data file values of one band of data against the values of another band.
  10. 10. Feature Space Multi-dimensional
  11. 11. Spectral Distance  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:
  12. 12. Image Classification Process Validation of the Result Definitions of the Clusters in the feature Space Selection of the Image Data
  13. 13. Classification Types  Common classification procedures can be broken down into two broad subdivisions based on the method used  Supervised Classification  Unsupervised Classification
  14. 14. Supervised Classification  The identity and location of some of the land cover types such as urban, agriculture , wetland are known as priori through a combination of field work and experience.  The analyst attempts to locate specific sites in the remotely sensed data that represent homogenous examples of these land cover types known as training sites.  Multivariate statistical parameters are calculated for these training sites.  Every pixel both inside and outside the training sites is evaluated and assigned to the class of which it has the highest likelihood of being a member.
  15. 15. Unsupervised Classification  The identities of land cover types to be specified as classes within a scene are generally not known as priori because ground reference information is lacking or surface features within the scene are not well defined.  The computer is required to group pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria.  Analyst then combine and re-labels the spectral clusters into information classes.
  16. 16. Supervised vs. Unsupervised Training  In supervised training, it is important to have a set of desired classes in mind, and then create the appropriate signatures from the data.  Supervised classification is usually appropriate when you want to identify relatively few classes, when you have selected training sites that can be verified with ground truth data, or when you can identify distinct, homogenous regions that represent each class.  On the other hand, if you want the classes to be determined by spectral distinctions that are inherent in the data so that you can define the classes later, the application is better suited to unsupervised training.  Unsupervised training enables you to define many classes easily, and identify classes that are not in contiguous, easily recognized regions.
  17. 17. Supervised Classification  In supervised training, you rely on your own pattern recognition skills and priori knowledge of the data to help the system determine the statistical criteria (signatures) for data classification.  To select reliable samples, you should know some information- either spatial or spectral-about the pixels that you want to classify.
  18. 18. Training Samples and Feature Space Objects  Training samples (also called samples) are sets of pixels that represent what is recognized as a discernible pattern, or potential class. The system calculates statistics from the sample pixels to create a parametric signature for the class. Selecting Training Samples - Training data for a class should be collected from homogenous environment. - If training data is being collected from n bands then, >10n pixels of training data is to be collected for each class.
  19. 19. There are a number of ways to collect training site data-  using a vector layer  defining a polygon in the image  using a class from a thematic raster layer from an image file of the same area (i.e, the result of an unsupervised classification)
  20. 20. Evaluation of Signatures  Ellipse – view ,Ellipse diagrams and scatterplots of data file values for every pair of bands.  There are tests to perform that can help determine whether the signature data are a true representation of the pixels to be classified for each class. You can evaluate signatures that were created either from supervised or unsupervised training.
  21. 21. Evaluation of Signatures  Signature separability is a satistical measure of distance between two signatures.  Separability can be calculated for any combination of bands that is used in the classification, enabling you to rule out any bands that are not useful in the results of the classification.
  22. 22. Selecting Appropriate Classification Algorithm  Various supervised classification algorithms may be used to assign an unknown pixel to one of the classes.  The choice of particular classifier depends on nature of input data and output required.  Parametric classification algorithms assume that the observed measurement vectors Xc, obtained for each class in each spectral band during the training phase are Gaussian in nature.  Non Parametric classification algorithms make no such assumptions.  There are many classification algorithms i.e. Parallelepiped, Minimum distance, Maximum Likelihood etc.
  23. 23. Parallelepiped Classification Algorithm  In the parallelepiped decision rule, the data file values of the candidate pixel are compared to upper and lower limits. These limits can be either:  1. The minimum and maximum data file values of each band in the signature,  2. the mean of each band, plus and minus a number of standard deviations, or  3. Any limits that you specify , based on your knowledge of the data and signatures.  There are high and low limits for every signature in every band. When a pixel’s data file values are between the limits for every band in a signature, then the pixel is assigned to that signature’s class.
  24. 24. Parallelepiped Classification Algorithm  Therefore, if the low and high decision boundaries are defined as-  Lck = ck – Sck and  Hck = ck +Sck  The parallelepiped algorithm becomes  Lck  BVijk  Hck
  25. 25. Overlap Region  In cases where a pixel may fall into the overlap region of two or more parallelepipeds, you must define how the pixel can be classified.  The pixel can be classified by the order of the signatures.  The pixel can be classified by the defined parametric decision rule.  The pixel can be left unclassified.
  26. 26. Advantages:  Fast and simple.  Gives a broad classification thus narrows down the number of possible classes to which each pixel can be assigned before more time consuming calculations are made.  Not dependent on normal distributions Disadvantages:  Since parallelepiped has corners, pixels that are actually quite far, spectrally from the mean of the signature may be classified.
  27. 27. Minimum Distance to Means Classification Algorithm  This decision rule is computationally simple and commonly used.  Requires mean vectors for each class in each band ck from the training data.  Euclidean distance is calculated for all the pixels with all the signature means Where ck and cl represent the mean vectors for class c measured in bands k and l  Any unknown pixel will definitely be assigned to one of any classes, there will be no unclassified pixel.
  28. 28. Advantages:  Since every pixel is spectrally closer to either one sample mean or other so there are no unclassified pixels.  Fastest after parallelepiped decision rule. Disadvantages  Pixels which should be unclassified will become classified.  Does not consider class variability.
  29. 29. Mahalanobis Decision Rule  Mahalanobis distance is similar to minimum distance, except that the covariance matrix is used in the equation.  Variance and covariance are figured in so that clusters that are highly carried lead to similar varied classes,
  30. 30. Advantages:  Takes the variability of classes into account unlike minimum distance or parallelepiped.  May be more useful than minimum distance in cases where statistical criteria ( as expressed in the covariance matrix ) must be taken into account. Disadvantages:  Tends to overclassify signatures with relatively large values in the covariance matrix.  Slower to compute than parallelepiped or minimum distance.  Mahalanobis distance is parametric, meaning that it relies heavily on a normal distribution of the data in each input band.
  31. 31. Maximum Likelihood/Bayesian Decision Rule  The maximum likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions.  If you have a priori knowledge that the probabilities are not equal for all classes, you can specify weight factors for particular classes. This variation of the maximum likelihood decision rule is known as the Bayesian decision rule ( Hord, 1982).
  32. 32.  The equation for the maximum likelihood/Bayesian classifier is as follows:
  33. 33. Advantages  The most accurate of the classifiers (if the input samples/clusters have a normal distribution, because it takes the most variable into consideration.  Takes the variability of classes into account by using the covariance matrix, as does Mahalanobis distance. Disadvantages  An extensive equation that takes a long time to compute. The computation time increases with the number of input bands.  Maximum likelihood is parametric, meaning that it relies heavily on a normal distribution of the data in each input band.  Tends to overclassify signatures with relatively large values in the covariance matrix.
  34. 34. Unsupervised Classification  It requires only a minimum amount of initial input from the analyst.  Numerical operations are performed that search for natural groupings of the spectral properties of pixels.  User allows computer to select the class means and covariance matrices to be used in the classification.  Once the data are classified, the analyst attempts a posteriori to assign these natural or spectral classes to the information classes of interest.  Some clusters may be meaningless because they represent mixed classes.  Clustering algorithm used for the unsupervised classification generally very according to the efficiency with which the clustering takes place.  Two commonly used methods are-  1. Chain method  2. Isodata clustering
  35. 35. Chain Method  Operates in two pass mode ( it passes through the registered multispectral dataset two times).  In the first pass, the program reads through the dataset and sequentially builds clusters.  A mean vector is associated with each cluster.  In the second pass, a minimum distance to means classification algorithm is applied to whole dataset on a pixel by pixel basis whereby each pixel is assigned to one of the mean vectors created in pass 1.  The first pass automatically creates the cluster signatures to be used by supervised classifier.
  36. 36. Pass 1: Cluster Building  During the first pass, the analyst is required to supply four types of information-  R, the radius distance in spectral space used to determine when a new cluster should be formed.  C, a spectral space distance parameter used when merging clusters when N is reached.  N, the number of pixels to be evaluated between each major merging of clusters.  Cmax , maximum no. of clusters to be identified. Pass 2: Assignment of pixels to one of the Cmax clusters using minimum distance classification logic.
  37. 37. Pass 2: Assignment of Pixels to one of the Cmax 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 Cmax clusters.
  38. 38. 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 an 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 of 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.
  39. 39. ISODATA algorithm normally requires the analyst to specify-  Cmax : maximum no. of clusters to be identified.  T: maximum % of pixels whose class values are allowed to be unchanged between iterations.  M: maximum no. of times isodata is to classify pixels and recalculate cluster mean vectors.  Minimum members in a cluster.  Maximum standard deviation for a cluster.  Split separation value (if the values is changed from 0.0, it takes the place of S.D).  Minimum distance between cluster means.
  40. 40. Phase 1: ISODSTA Cluster Building using many passes through the dataset
  41. 41. (a) Distribution of 10 ISODATA mean vectors after just one iteration. (b) Distribution of 20 ISODATA mean vectors after 20 iterations. The bulk of the important feature space (the gray background) is partitioned rather well after just 20 iterations.
  42. 42. Accuracy Assessment  Accuracy assessment is a general term for comparing the classification to geographical data that are assumed to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. Error Matrix  Once a classification has been sampled a contingency table (also referred to as an error matrix or confusion matrix) is developed.  - This table is used to properly analyze the validity of each class as well as the classification as a whole.  In this way, in more detail the efficiency of the classification can be evaluated.
  43. 43. Accuracy Assessment  One way to access accuracy is to go out in the field and observe the actual land class at a sample of locations, and compare to the land classification it was assigned on the thematic map.  There are a number of ways to quantitatively express the amount of agreement between ground truth classes and the remote sensing classes.  One way is to construct a confusion matrix, alternatively called a error matrix  This is a row by column table, with as many rows as columns.  Each row of the table is reserved for one of the information, or remote sensing classes used by the classification algorithm.  Each column displays the corresponding ground truth classes in an identical order. Ground truth classes No. classified A B C pixels Thematic map classes A 35 2 2 39 B 10 37 3 50 C 5 1 41 47 No. ground truth pixels 50 40 46 136
  44. 44. Accuracy Assessment  Comparison to two sources of information - Remote Sensing derived classification map - Reference Test information  The relationship between the two sets of information’s is expressed as a matrix known as Error Matrix / Confusion Matrix/ Contingency Table Classified Image Reference Data
  45. 45. Overall Accuracy  The diagonal elements totally the number of pixels classified correctly in each class.  An overall measure of classification accuracy is – Total number of correct classifications total number of classifications which in this example amounts to (35+37+41)/136 or 83%.  But, just because 83% classifications were accurate overall, does not mean that each category was successfully classified at that rate.
  46. 46. Users Accuracy  A user of the imagery who is particularly interested in class A, say, might wish to know what proportion of pixels assigned to class A were correctly assigned.  In this example, 35 of the 39 pixels were correctly assigned to class A, and the user accuracy in this category of 35/39 = 90%. Ground truth classes No. classified A B C pixels Thematic map classes A 35 2 2 39 B 10 37 3 50 C 5 1 41 47 No. ground truth pixels 50 40 46 136 Number of diagonal cell of error matrix Number in row total
  47. 47. Producers Accuracy  Contrasted to user accuracy is producer accuracy, which has a slightly different interpretation.  Producers accuracy is a measure of how much of the land in each category was classified correctly.  It is found, for each class or category, as Ground truth classes No. classified A B C pixels Thematic map classes A 35 2 2 39 B 10 37 3 50 C 5 1 41 47 No. ground truth pixels 50 40 46 136 Number of diagonal cell of error matrix Number in row total • The producer’s accuracy for class A is 35/50 = 70 %
  48. 48.  So from this assessment, there are three measures of accuracy which address subtly different issues:  Overall accuracy: takes no account of source of error ( errors of omission or commission)  User accuracy: measures the proportion of each TM class which is correct.  Producer accuracy: measures the proportion of the land base which is correctly classified.
  49. 49. KAPPA Coefficient  Another measure of map accuracy is the kappa coefficient, which is a measure of the proportional (or percentage) improvement by the classifier over a purely random assignment to classes.  For an error matrix with r rows, and hence, the same number of columns, let A = the sum of r diagonal elements, which is the numerator in the computation of overall accuracy. Let B= sum of the r products (row total x column total). Kappa hat = (NA – B) / N2 - B
  50. 50. Land Use/Land Cover  Land cover data documents how much of a region is covered by forests, wetlands, impervious surfaces, agriculture, and other land and water types. Water types include wetlands or open water. Land use shows how people use the landscape – whether for development, conservation, or mixed uses. The different types of land cover can be managed or used quite differently.  Land cover can be determined by analyzing satellite and aerial imagery. Land use cannot be determined from satellite imagery. Land cover maps provide information to help managers best understand the current landscape. To see change over time, land cover maps for several different years are needed. With this information, managers can evaluate past management decisions as well as gain insight into the possible effects of their current decisions before they are implemented.
  51. 51. Thematic Maps  Thematic maps are single-topic maps that focus on specific themes or phenomena, such as population density, rainfall and precipitation levels, vegetation distribution, and poverty.  This differs from reference maps which include a number of different elements like roads, topography, and political boundaries.
  52. 52. References • Thomas M. Lillesand & Ralph W. Kiefer , “Remote sensing and Image Interpretation”, Seventh Edition, 2004. • Lectures from EDUSAT IIRS Dehradun, India. • Online resources from internet and books and materials from various sources. 61