Chapter 11 Image Processing - Classification


11.1 Classification Techniques

Classification of remotely sensed data is used to assign corresponding levels with respect to groups with homogeneous characteristics, with the aim of discriminating multiple objects from each other within the image.

The level is called class. Classification will be executed on the base of spectral or spectrally defined features, such as density, texture etc. in the feature space. It can be said that classification divides the feature space into several classes based on a decision rule. Figure 11.1.1 shows the concept of classification of remotely sensed data

. In many cases, classification will be undertaken using a computer, with the use of mathematical classification techniques. Classification will be made according to the following procedures as shown in Figure 11.1.2.

Step 1: Definition of Classification Classes
Depending on the objective and the characteristics of the image data, the classification classes should be clearly defined.

Step 2: Selection of Features
Features to discriminate between the classes should be established using multi-spectral and/or multi-temporal characteristics, textures etc.

Step 3: Sampling of Training Data
Training data should be sampled in order to determine appropriate decision rules. Classification techniques such as supervised or unsupervised learning will then be selected on the basis of the training data sets.

Step 4: Estimation of Universal Statistics
Various classification techniques will be compared with the training data, so that an appropriate decision rule is selected for subsequent classification.

Step 5: Classification
Depending up on the decision rule, all pixels are classified in a single class. There are two methods of pixel by pixel classification and per-field classification, with respect to segmented areas.

Popular techniques are as follows.
a. Multi-level slice classifier
b. Minimum distance classifier
c. Maximum likelihood classifier
d. Other classifiers such as fuzzy set theory and expert systems

Step 6: Verification of Results
The classified results should be checked and verified for their accuracy and reliability.


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