The document discusses different types of classification techniques. It categorizes classification based on the training sample, parameters used, pixel/spatial elements analyzed, use of spatial information, and whether multiple classifiers are used. Specific algorithms are listed for each category. Common classification techniques include maximum likelihood, k-means clustering, linear discriminant analysis, artificial neural networks, support vector machines, and decision trees. Each technique has distinct characteristics such as using parametric or non-parametric approaches and handling different types and volumes of input data.

1. Classification types
S.No. Classification Basedon Types Algorithm used
1 Training sample based Supervised
Classification
Parallel pipelined
algorithm, maximum
likelihood algorithm,
minimum distance to mean
algorithm
Unsupervised
classification
K means clustering
algorithm
2 Parameter based Parametric
classifier
Maximum likelihood
algorithm, linear
discriminant analysis
Non parametric
classifier
Artificial Neural
Network(ANN), Support
vector machine (SVM),
Decision tree classifier
3 Pixel based Per pixel
classifier
Maximum likelihood
algorithm, Artificial
Neural Network(ANN),
Support vector machine
(SVM)
Sub pixel
classifier
Spectral mixture analysis,
sub pixel classifier, fuzzy
set classifier
Per-field classifier GIS based classification
approaches
Object oriented eCognition
4 Spatial element Hard
classification
maximum likelihood
algorithm, minimum
distance, Artificial Neural
Network(ANN), Support
vector machine (SVM),
Decision tree algorithm
Soft classification Fuzzy algorithm
5 Spatial information Spectral classifier maximum likelihood
algorithm, minimum
distance, Artificial Neural
Network(ANN)
Contextual
classifier
Frequency-based
contextual classifier
Spectral –
contextual
classifier
Combination of parametric
or non-parametric and
contextual algorithm
6 Multiple classifier Voting rules, Bayesian
formalism, evidential
reasoning, multiple neural
network.

2. Classification Techniques
Classification method Description Characteristics
Artificial Neural network ANN is a type of artificial
intelligence that imitates
some functions of the person
mind. ANN has a normal
tendency for storing
experiential knowledge. An
ANN consists of a sequence
of layers, each layer consists
of a set of neurones. All
neurones of every layer are
linked by weighted
connections to all neurones
on the preceding and
succeeding layers
It uses Nonparametric
approach. Performance and
accuracy depends upon the
network structure and
number of inputs
Decision tree DT calculates class
membership by repeatedly
partitioning a dataset into
uniform subsets Hierarchical
classifier permits the
acceptations and rejection of
class labels at each
intermediary stage. This
method consists of 3 parts:
Partitioning the nodes, find
the terminal nodes and
allocation of class label to
terminal nodes
DT are based on hierarchical
rule based method and use
Nonparametric approach.
Support Vector Machine A support vector machine
builds a hyper plane or set of
hyper planes in a high- or
infinite dimensional space,
used for classification. Good
separation is achieved by the
hyper plane that has the
largest distance to the nearest
training data point of any
class (functional margin),
generally larger the margin
lower the generalization error
of the classifier.
SVM uses Nonparametric
with binary classifier
approach and can handle
more input data very
efficiently. Performance and
accuracy depends upon the
hyper plane selection and
kernel parameter.