supervised classification

SUPERVISED CLASSIFICATION

Course: Introduction to RS & DIP

Mirza Muhammad WaqarContact:

mirza.waqar@ist.edu.pk+92-21-34650765-79 EXT:2257

RG610

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Contents

Hard vs Soft Classification Supervised Classification

Training Stage Field Truthing Inter class vs Intra Class Variability

Classification Stage Minimum Distance to Mean Classifier Parallelepiped Classifier Maximum Likelihood Classifier

Output Stage Supervised vs Unsupervised Classification

Hard vs Soft Classification

Hard Classification In hard classification, we can assign mixed pixels

are pure pixels. It means we create an additive error in our pure class.

• Soft Classification In soft classification, for mix pixels, we identify the

dominance and co-dominance factors in pixel. Through this analysis we can identify at the most three classes in one pixel. Though this analysis we can’t identify a class that is contributing less than 20% in the pixel.

Supervised Classification

Such Classification, in which human interruption involve.

Totally human decision dependent. Analyst define training sites, and on the base of

these training sites, clusters formed.

Supervised Classification

There are three phase in supervised classification. Training stage Classification stage Output stage

Training Stage

Clear objective of classification Experiment on the image for understanding

different land covers exit in the image. Identify the major variations in the image (hot

spots). Any spectral variation that is new for analyst. Create multiple false color composites of ground

truthing area. Ground truthing for hot spots identification.

Field Truthing

Alternate for not accessible hot spots Historical data Local person’s knowledge High resolution imagery

Inter-Class Variability vs Intra-Class Variability

Inter-Class Variability It means variability among different classes in

satellite image. Separating different land cover classes in satellite

image. Accuracy of classification is dependent on inter-

class variability/separability.

Inter Class Variability

Intra-Class Variability

Within class variability. Used to map sub types of land covers, e.g. forest,

bare soil, rocks etc. Feature space is a useful tool for within-class

variability but the prediction through feature space is totally dependent on spectral signature.

An appropriate feature space should be choose for intra-class variability.

Classification Stage

There are three classifier. Minimum Distance to Mean Classifier Parallelepiped Classifier Maximum Likelihood Classifier

Minimum Distance to Mean Classifier

Parallelepiped Classifier

Maximum Likelihood Classifier

Output Stage

In output stage, we define the level of classification.

Create final classes. Accuracy Assessment Area estimation.

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Supervised vs. Unsupervised

Edit/evaluate

signatures

Select Training fields

Classify image

Evaluate classification

Identify classes

Run clustering algorithm

Evaluate classification

Edit/evaluate signatures

Questions & Discussion