Unsupervised Classification

UNSUPERVISED CLASSIFICATION

Course: Introduction to RS & DIP

Mirza Muhammad WaqarContact:

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

RG610

2

Contents

Satellite Image Classification Spectral vs Spatial Pattern Recognition Feature Space Image Spectral Signature Limitations in Classification Unsupervised Classification

Clustering Algorithms K-means Approach ISODATA Model Texture or Roughness Model

Post Classification

Satellite Image Classification

Automatic categorization of all pixel into land cover classes.

To explore different land covers in a satellite image. To assign unique no or symbol to particular land

cover. Qualitative and Quantitative analysis of satellite

image.

Spectral vs Spatial

Spectral pattern recognitionFamily of classification procedures that utilize pixel by pixel spectral information as the basis for automated land cover classification.

Spatial pattern recognitionCategorization of image pixels on the basis of their spatial relationship with pixels surrounding them.

Feature Space

Feature Space

Image Spectral Signatures

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TM Band #

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Satellite Image Classification

Three types of classifications

Un-Supervised Classification Supervised Classification Hybrid Classification

Limitations in Classification

Limitations face in satellite image classification Pixel Size All Statistical parameters are developed for

normalized distribution.

Un-Supervised Classification

Use to cluster pixels based on statistics only. No user defined training classes required. Machine based classification. Post classification is of more importance to make

results meaningful. Incorporate all the natural groups in satellite image

(spectral classes). Un-supervised Classification have two phases.

Clustering Post Classification

Clustering Algorithms

Numerous clustering algorithms K-means Approach ISODATA Model Texture or Roughness Model

K-means Approach

Accept number of clusters to be located in the data.

Arbitrarily locate that number of cluster centers in multi-dimensional measurement space.

Each pixel is assigned to the cluster whose mean vector is closest.

Band A

Band B

Band A

Band B

New computed MeansPrevious Means

Band A

Band B

New computed MeansPrevious Means

K-Mean Approach

New means are computed. Revised clustering on the base of new computed

means. This process continue until there is no significant

change in clusters mean.

ISODATA Model

Iterative Self-Organizing Data Analysis. Follow K-mean principle for clustering. Accept number of clutters, number of iterations &

convergence tolerance from the user and form clusters.

Permits number of clusters to change from one iteration to the next, by merging, splitting and deleting clusters based on spatial statistics and user defined conditions.

Band A

Band B

New computed MeansPrevious Means

Texture or Roughness Model

It incorporate a sensitivity to image “texture” or “roughness” as a basis for establishing clusters centers.

Texture is computed through multi-dimensional variance observed in moving window (e.g. a 3x3 window).

Analyst sets a variance threshold below which window is consider “smooth” and above which it is considered “rough”.

Texture or Roughness Model

The mean of the first smooth window encountered in the image becomes the first cluster center.

The mean of the second smooth window encountered in the image becomes the second cluster center and so forth.

This process continue until the user defined no of clusters reached.

Post Classification

In post classification phase, analyst compare spectral classes with some reference data to determine the identify of the spectral classes.

Spectral reflectance curves can be used to identify the spectral classes.

Defining the level of classification Merging different classes to reach final outcome. Accuracy assessment through field truthing.

Spectral Classes Identity of Spectral Class Corresponding Desired Information Category

Possible outcome 1 Water Water

1 Coniferous trees Coniferous trees

2 Broad Leave trees Broad Leave trees

3 Bare Soil Bare Soil

4 Rocks Rocks

5 Built-up Area Built-up Area

Possible outcome 2

1 Water Water

2 Coniferous trees Forest

3 Broad Leave trees

4 Bare Soil Open Land

5 Rocks

6 Roads Built-up Area

7 Urban Area

Questions & Discussion