For engineering

1. Digital Image Classification
Binod Humagain
Instructor, LMTC

2. Contents
 Introduction
 Spectral Signature
 Training of Classifiers
 Feature Selection and Separability Analysis
 Image Classification Techniques
Unsupervised Classification
Supervised Classification
Object Based Classification(OBIA)
 Accuracy Assessment

3. Remote Sensing Process

4. Image Classification
 Grouping of Similar features
 Separation of dissimilar one
 Assigning class level to pixel
 Resulting in manageable size of class

6. Image Classification
 Digital image classification uses the spectral information
represented by the digital numbers in one or more spectral
bands, classify each individual pixel based on this spectral
information.
 multispectral data are used to perform the classification, and the
spectral pattern present within the data for each pixel is used as
numerical basis for categorization.

7. Advantage of Image Classification
 To analyze thematic characteristics of object based on
brightness values in image
 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 data set

8. Disadvantage of Image classification
 No use of other characteristics such as location, orientation,
pattern, texture . . .
 Exist spectral overlap i.e. heterogeneous classes, mixed pixels
(boundaries),…
 No categories in land use and land cover

9. Need of Image Classification
 Cost efficient in the analyses of large data sets
 Results can be reproduced
 More objective then visual interpretation
 Effective analysis of complex multi-band (spectral)
interrelationships
 Classification achieves data size reduction together with manual
digitizing

10. Classification Methods

11. Spectral Signature
The set of discrete
spectral radiance
measurements
provided by the broad
spectral bands of the
sensor define the
spectral signature of
each class

12. Dimension 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.
 In addition to raw bands, we can also use derived products like NDVI images
and PCA Image

13. Measurement Vector
 The measurement vector of a pixel is the set of data file values for one pixel
in all n bands.
 Although image data files are stored band-by band, it is often necessary to
extract the measurement vectors for individual pixels.
Generalized Measured
Vector or Feature Vector

14. 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.
Generalized
Mean Vector

15. Feature Space
 A feature space image is a scatter plot of the pixel values of two bands of the
imagery.
 The intensity of each portion of the feature space image is simply the number
of pixels in the image that have that particular pair of x, y brightness values.
 The more intense the color, the higher the density of pixels with that particular
combination of brightness's
 reds, yellows, and oranges indicate high densities
 the black, purple and blue, lower densities.
 A feature space image that compares uncorrelated bands such as infrared to
visible usually gives a better feel for the partitioning than a feature space image
comparing two correlated bands.

16. Feature Space
Feature Space Plot displaying
high correlation between 2
bands of a satellite image
Feature Space Plot displaying low
correlation between 2 bands of a
satellite image

17.  Each feature vector is a point in the so-called 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.
 N = the number of bands = dimensions …. an (n) dimensional data (feature)
space.
 Features can be
 Raw bands
 Derived Images
Feature Space

18.  Similar objects yields similar measurement results (feature vectors) i.e. nearby
points in feature space correspond to similar objects
 Distance in feature space is related to dissimilarity
 Points that belong to the same class form a cloud in feature space
Feature Space

20.  Find and determine class a clusters
 Decide on decision boundaries
 Assign a class to each pixel
Partition of Feature Space

21. Classification Types
 Common classification procedures can be divided into
 Supervised classification
 Unsupervised classification
 Object Based Image Classification(OBIA)

22. Supervised Classification
 Samples of known identity are used to classify pixels of unknown identity
 Multivariate statistical parameters are calculated for the known samples.
 Every pixel is evaluated and assigned to the class which it most closely resemble
digitally (in statistics).
 Hard Classification – a pixel is assigned to only one class.
 Called as supervised as the analyst supervises the algorithm by providing training
sites

23. Unsupervised Classification
 The identities of land cover types (to be specified as classes) within a scene are generally not
known a priori because either 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.

24. Object Based Image Classification
 Object-Based Image Analysis (OBIA)- also called Geographic Object-Based Image
Analysis (GEOBIA).
 OBIA is a sub-discipline of geoinformation science devoted to partitioning remote
sensing imagery into meaningful image-objects, and assessing their characteristics
through spatial, spectral and temporal scale.
 The fundamental step of any object-based image analysis is a segmentation of a
scene-representing an image-into image objects.

25. Supervised Classification

26. Supervised Classification: Overview
 The identity and location of some of the land cover types are known a priori through a
combination of field work and experience.
 These locations or Samples or training sites or training areas are the homogeneous
representative samples of the different surface cover types (information classes) of
interest.
 The analyst supervises the algorithm by providing these training sites (Samples of known
identity ) which are used to classify pixels of unknown identity
 Multivariate statistical parameters are calculated for these training sites. Every pixel is
evaluated and assigned to the class which it most closely resemble digitally (in statistics).
 Hard Classification – a pixel is assigned to only one class.

27. Steps in Supervised Classification

28.  Training samples (also called samples) are sets of pixels that represent what is
recognized as a Potential class.
 The system calculates statistics from the sample pixels to create a parametric
signature for the class.
 It should be well distributed over the entire scene.
Training Sample

31.  Training data for a class should be collected from homogeneous
environment.
 Each site is usually composed of many pixels.
 Size: The general rule is that if training data is being collected from n bands
then >10n pixels of training data is to be collected for each class
 This is sufficient to compute variance covariance matrices required by some
classification algorithms.
Selecting Training Sample

32.  Remotely sensed data is highly correlated in nature
 Selection of variables showing strong discriminatory power and hence to avoid
redundancies in feature set is termed as feature selection or feature extraction and
is a key problem in classification
 Select a subset of features that are most discriminative.
 Select features which show less overlap between classes and hence are more
discriminative.
Feature Selection

33. Separability Analysis
 The highly correlated bands are rejected, and those with lesser or no correlation
are selected for efficient analysis.
 Feature selection may involve both statistical and graphical analysis to determine
the degree of between-class separability in the remote sensor training data.
 Using statistical methods, combinations of bands are normally ranked according to
their potential ability to discriminate each class from all others using n bands at a
time.

34. Selection of Appropriate 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:
 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.

43. Unsupervised Classification

44.  Unsupervised classification is the process where numerical operations are performed that
search for natural groupings of the spectral properties of pixels, as examined in multispectral
feature space.
 The clustering process results in a classification map consisting of m spectral classes.
 The analyst then attempts a posteriori (after the fact) to assign or transform the spectral
classes into thematic information classes of interest (e.g., forest, agriculture).
 This may be difficult. Some spectral clusters may be meaningless because they represent
mixed classes of Earth surface materials.
 The analyst must understand the spectral characteristics of the terrain well enough to be able
to label certain clusters as specific information classes.
 Advantages of unsupervised classification is no extensive knowledge of the study area is
required. Little user input is needed to perform unsupervised classification which minimizes the
likelihood of human error. However, the analyst has little control of the classes generated and
often these clusters contain multiple land covers making interpretation difficult
Unsupervised Classification: Overview

45.  Clustering parameters
 number of clusters
 size of cluster
 distance between the cluster centers
 cluster elimination value (threshold)
 Methods
 Unsupervised Slicing
 K-means clustering
 Clustering using the Iterative Self-Organizing Data Analysis Technique (ISODATA).
Unsupervised Classification Technique

46. Unsupervised Slicing

48. K-Means Algorithm
 Iterative algorithm
 Number of cluster K is known by user
 Most popular clustering algorithm
 Initialize randomly K cluster mean vectors
 Assign each pixel to any of the K clusters based on minimum feature distance
 After all pixels are assigned to K clusters, each cluster mean is recomputed
 Iterate till cluster mean vectors stabilize

49. ISODATA Algorithm
 Iterative Self-Organizing Data Analysis Technique
 Generalization of K-Mean algorithm
 Consists of many user-specified parameters
 Minimum size of cluster
 Maximum size of cluster
 Maximum intra-cluster variance
 Minimum separation between pairs of clusters
 Maximum number of clusters
 Minimum number of clusters
 Maximum number of iteration

50. Unsupervised Classification Advantage,
Disadvantages
 Advantages
 No prior knowledge is required
 Human Error is minimized
 Unique classes are recognized
 Disadvantages
 The output classes are spectral classes which we cannot exactly say as information classes
and imposes constrain to our interpretation
 Additional Labelling is required
 Spectral properties vary over time, across image

51. Supervised vs. Unsupervised Classification
 UNSUPERVISED APPROACH
 Considers only spectral distance measures
 Minimum user interaction
 Requires interpretation after classification
 Based on spectral groupings
 SUPERVISED APPROACH
 Incorporates prior knowledge
 Requires training set(samples)
 Based on spectral groupings
 More extensive user interaction

52. Classification:
Object Based Image Analysis(OBIA)

53. Object Based Image Classification
 Object-Based Image Analysis (OBIA)- also called Geographic Object-Based Image
Analysis (GEOBIA).
 OBIA is a sub-discipline of geoinformation science devoted to partitioning remote
sensing imagery into meaningful image-objects, and assessing their characteristics
through spatial, spectral and temporal scale.
 The fundamental step of any object-based image analysis is a segmentation of a
scene-representing an image-into image objects.

54. Object based image classification/Analysis
 Supervised and unsupervised classification is pixel-based; In other words, it creates
square pixel and each pixel has a class; But object based classification groups pixels
into representative vector shapes with size and geometry.
 Steps to perform object-based image analysis classification:
 Perform multiresolution segmentation
 Select training areas
 Define statistics
 Classify

55. Object based image Analysis
 Object-based image analysis (OBIA) segments an image by
grouping pixels
 It doesn’t create single pixel. Instead, it generates objects with
different geometries
 If you have the right image, objects can be so meaningful that it
does the digitizing for you.
 For example, the segmentation results below highlights buildings
 The 2 most common segmentation algorithms are:
 Multiresolution segmentation in Ecognition
 Segment mean shift in ArcGIS

56. Object based image Analysis
 In Object-Based Image Analysis (OBIA) classification, you can use different methods
to classify object. E.g. Can use
 SHAPE: if you want to classify buildings, you can use a shape statistic such as
“rectangular fit”, this test an object’s geometry to the shape of a rectangle
 TEXTURE: Texture is the homogeneity of an object. For example, water is mostly
homogeneous because it’s mostly dark blue. Bust forests have shadows and are
mix of green and black
 SPECTRAL: You can use the mean value of spectral properties such as near-infrared,
short-wave infrared, red, green or blue
 GEOGRAPHIC CONTENT: Objects have proximity and distance relationships
between neighbors.

57. OBIA: Nearest Neighbor Classification
 Nearest Neighbor(NN) classification is similar to supervised classification
 After multi-resolution segmentation, the user identifies sample sites for each land
cover class.
 Next, they define statistics to classify image objects
 Finally, nearest neighbor classifies objects based on their resemblance to training
sites and the statistics defined

58. Accuracy Assessment

59. “A classification is not complete until its accuracy
has been assessed” Why?
 Curiosity: the desire to know how good something
is.
 Crucial to know the quality of maps when using
them to make resource decisions.
 Increase map quality by identifying and correcting
sources of error.
 can compare the accuracy of various data,
processing techniques, classification schemes,
and interpreters.
Accuracy Assessment

60.  Comparison to two sources of information
 Remote Sensing derived classification map
 Reference Test information
 The relationship between the two sets of
information is expressed as a matrix known as
 Error matrix / confusion matrix /Contingency
table
Accuracy Assessment

61. Selection of Sample Data: Sampling
Scheme
 A combination of the following factors
 Class definition
 Design of sampling technique
 Determination of no. of samples for each class
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62. Sampling Scheme?
 Questions concerning design of sample data:
 What are the map classes to be assessed and how are they distributed across the
landscape?
 How many sample should be taken
 How should the samples be chosen
 Question concerning how the reference data should be collected
 What should be the source of reference data?
 How should reference data be collected?
 When should the reference data be collected?
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63. Reference Data
Issue 1: Choosing reference source
 Make sure you can actually extract from the reference source the information that you need
for the classification scheme
 I.e. Aerial photos may not be good reference data if your classification scheme distinguishes four
species of grass. You may need GPS’d ground data.
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64. Reference data
Issue 2: Determining size of reference plots
 Consider the extent and distribution of class
 i.e. take larger sample size for the features that cover larger area
 Consider heterogeneity in the scene
 i.e. increase sample size when the variation within the class is more
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65. Sampling Methods
Simple Random Sampling :
observations are randomly placed.
Stratified Random Sampling
minimum number of observations
are randomly placed in each
category.
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66. Sampling Methods
Systematic Sampling
Observations are placed at equal intervals
according to a strategy.
Systematic Non-Aligned Sampling
:
a grid provides even distribution of
randomly placed observations.
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67. Sampling Methods
Cluster Sampling
Randomly
placed “centroids” used as a base
of several nearby observations.
The nearby observations can be
randomly selected, systematically
selected, etc...
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68. 68

69. Confusion Matrix

70. Errors

71. Commission Error

72. Omission Error

73. Representation of Accuracy

74. Overall Accuracy

75. Producer’s Accuracy

76. User’s Accuracy

77. Accuracy Assessment

78. Thank You