1. University College London (UCL), UK
Welcome
to the presentation on
Image Classification
BAYES AHMED
PhD Student
University College London (UCL), UK
Training at BUET-JIDPUS
04 June 2014

2. Image Classification
Here IMAGE stand for Digital/Raster Image (e.g. Satellite Image)
In general in GIS, we use two types of Image Classification:
Pixel Based
Objected-Oriented Segment-Based

3. Pixel
Pixel is a physical point (e.g. dot), or the smallest addressable element (e.g. cell) in a raster image

4. Pixel Resolution

5. Landsat Satellite Images

6. Image Composition
Computer screens can display an image in three different bands at a time, by using a different primary color for each band

7. False Color Composition (FCC)

8. Digital Image Classification
Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information.

9. Information and Spectral Classes
Information Classes are those categories of interest that the analyst is actually trying to identify in the imagery.
Spectral Classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the different spectral channels of the data.
A broad information class may contain a number of spectral sub-classes with unique spectral variations. It is the analyst's job to decide on the utility of the different spectral classes and their correspondence to useful information classes.

10. Supervised Classification
There are two general approaches to pixel-based image classification: supervised and unsupervised.
Supervised Classification: the analyst identifies in the imagery homogeneous representative samples (information classes) of interest. These samples are referred to as training areas.
The selection of appropriate training areas is based on the analyst's familiarity with the geographical area and their knowledge of the actual surface cover types present in the image. Thus, the analyst is "supervising" the categorization of a set of specific classes.

11. Supervised Classification
Information classes (i.e., landcover types)
The software system is then used to develop a statistical characterization/ algorithm (mean, variance and covariance) of the reflectances for each information class. This stage is often called signature development.

12. Supervised Classification
The image is then classified by examining the reflectances for each pixel and making a decision about which of the signatures it resembles most. There are several techniques for making these decisions, called classifiers.
Classifiers: Minimum distance to means (MINDIST), maximum likelihood (MAXLIKE), linear discriminant analysis (FISHER), Bayesian (BAYCLASS), multi-layer perceptron (MLP) neural network, self-organizing map (SOM) neural network; Mahalanobis typicalities (MAHALCLASS), Dempster- Shafer belief (BELCLASS), linear spectral unmixing (UNMIX), fuzzy (FUZCLASS), spectral angle mapper (HYPERSAM), minimum distance to means (HYPERMIN), linear spectral unmixing (HYPERUNMIX), orthogonal subspace projection (HYPEROSP), and absorption area analysis (HYPERABSORB) etc.

13. Maximum Likelihood
The maximum likelihood classifier calculates for each class the probability of the cell belonging to that class given its attribute values. Each pixel is assigned to the class that has the highest probability (that is, the maximum likelihood).

14. Final Output (Image Classification)

15. Unsupervised Classification
Unsupervised classification reverses the supervised classification process. It is not completely without human intervention.
Spectral classes are grouped first, based solely on the numerical information in the data, and are then matched by the analyst to information classes (if possible).
Programs, called clustering algorithms, are used to determine the natural (statistical) groupings or structures in the data.

16. Unsupervised Classification

17. Supervised vs. Unsupervised Classification

18. Supervised vs. Unsupervised Classification

19. Object-Oriented Segment-Based Classification (OOSBC)
These pixel- based procedures analyze the spectral properties of every pixel within the area of interest, without taking into account the spatial or contextual information related to the pixel of interest.
OOSBC analyzes both the spectral and spatial/contextual properties of pixels and use a segmentation process and iterative learning algorithm to achieve a semi-automatic classification. It considers – shape, size, color, texture, shadow, site, association and pattern.

20. Object-Oriented Segment-Based Classification (OOSBC)

21. Thank You All, QUESTIONS?