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FEATURE EXTRACTION

Feature Extraction uses an object-based approach to classify imagery, where an object (also called segment) is a group of pixels with similar spectral, spatial, and/or texture attributes. Traditional classification methods are pixel-based, meaning that spectral information in each pixel is used to classify imagery. With high-resolution panchromatic or multispectral imagery, an object-based method offers more flexibility in the types of features to extract.
Feature Extraction uses an object-based approach to classify imagery, where an object (also called segment) is a group of pixels with similar spectral, spatial, and/or texture attributes. Traditional classification methods are pixel-based, meaning that spectral information in each pixel is used to classify imagery. With high-resolution panchromatic or multispectral imagery, an object-based method offers more flexibility in the types of features to extract.

The workflow involves the following steps: 


Dividing an image into segments

Computing various attributes for the segments

Creating several new classes

Interactively assigning segments (called training samples) to each class

Classifying the entire image with a K Nearest Neighbor (KNN), Support Vector Machine (SVM), or Principal Components Analysis (PCA) supervised classification method, based on your training samples.

Exporting the classes to a shapefile or classification image.