Atmospheric remote sensing toolkit

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BRG The University of Queensland
Fundamentals

- Main page
- Pre-processing
- Field data collection
- Mapping approaches
  - Manual delineation
  - Object based analysis
  - Supervised class.
  - Unsupervised class.
  - Inverse modelling
  - Regression analysis
- Change and trends
- Delivery

# Unsupervised classification

### How does unsupervised classification work?

Unsupervised classification, uses multi-variate clustering algorithms to group with similar spectral reflectance signatures.

Field data or knowledge are then used to assign a mapping category label to each cluster.

Unsupervised classification sequence: A) Corrected image, B) Unsupervised classification based on pixel statistics, C) Field data for assigning mapping categories to clusters of pixels or for validation, and D) Labelled unsupervised classification

## References:

Jensen, J. R. (2005). Introductory digital image processing: a remote sensing perspective. Third edition, Prentice Hall: 316.