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Procedure on Autonomous Sampling and Reductive Learning in Imagery.
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HaSa documentation |
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1 Introduction
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1.1 Sample/demo data
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1.2 Machine learning classifiers
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2 How to start/Input
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2.1 Handling of image data
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2.1.1 Data structure
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2.1.2 Variable input imagery
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2.1.3 Variable image categories
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2.2 Selecting reference samples
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2.2.1 Selecting refernence points
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2.2.2 Selecting reference polygons
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2.3 Selecting class names
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3 Generating outputs
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3.1 Calculating class probability
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3.1.1 Choosing thresholds
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3.1.2 Interactive probability maps
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3.2 Generating image classification
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3.2.1 Sample distributions
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3.2.2 Spatial statistics
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