Supervised Classification
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Transcript of Supervised Classification
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SUPERVISED CLASSIFICATION
Course: Introduction to RS & DIP
Mirza Muhammad WaqarContact:
[email protected]+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
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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.
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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.
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Supervised Classification
There are three phase in supervised classification. Training stage Classification stage Output stage
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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.
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Field Truthing
Alternate for not accessible hot spots Historical data Local person’s knowledge High resolution imagery
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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.
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Inter Class Variability
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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.
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Classification Stage
There are three classifier. Minimum Distance to Mean Classifier Parallelepiped Classifier Maximum Likelihood Classifier
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Minimum Distance to Mean Classifier
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Parallelepiped Classifier
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Maximum Likelihood Classifier
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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
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Questions & Discussion