remote sensing Image classification

1. Outline
- The Classification Stage
- The Training Stage
- Supervised, Unsupervised And Hybrid Classification
- Classification Of Mixed Pixels
- The Output Stage And Post-classification Smoothing
- Object-Based Classification
- Neural Network Classification
- Classification Accuracy Assessment
1

2. Image Classification
2
• The overall objective of image classification procedures is to
automatically categorize all pixels in an image into land cover classes or
themes, this is done using
Spectral pattern recognition;
– that is, pixels that share similar combinations of spectral reflectance or
emissivity are grouped together in classes that are assumed to represent
particular categories of surface features.
Spatial pattern recognition
– is a very different approach, involving the categorization of image pixels
on the basis of their spatial relationship with pixels surrounding them.
– much more complex and computationally intensive than spectral
pattern recognition procedures.
object-based image analysis
– involves combined use of both spectral and spatial pattern
recognition.

3. 3
Image Classification Algorithm

4. 4
• Classification the image analyst “supervises” the pixel categorization process
• There are three basic steps involved in a typical supervised classification
procedure.
1. training stage , the analyst identifies representative training areas and
develops a numerical description of the spectral attributes of each land
cover type of interest in the scene.
2. Classification stage each pixel in the image data set is categorized into
the land cover class it most closely resembles. If the pixel is insufficiently
similar to any training data set, it is usually labeled “unknown.”
3. output stage the results may be used in a number of different ways. Three
typical forms of output products are thematic maps, tables of statistics for
the various land cover classes.
Supervised classification

5. 5
Land cover classification systems
• The ITC land use and land cover classification approach (LUCC);
• Seven main land cover categories are distinguished
• The CORINE(Coordination of information on the environment) land
cover program
• major five land cover categories
• The FAO Land Cover Classification System .
• eight major land cover types are defined

6. 6
Land use/land cover classes

7. 7
Training Stages
• Supervised classification relies on rules to decide into which map class a
pixel or an object will be placed
• All supervised classification methods require examining sample areas of
the imagery where the map class (according to the classification scheme)
is known
• Great care must be taken to efficiently and effectively generate
appropriate training sites for each map class. Poor selection of training
sites can result in producing an inaccurate map because the classifier will
be “poorly trained,”
• There are many methods for delineating training sites
– rectangle to select a group of pixels (larger than the grid size)
– draw a polygon rather than just a rectangle (larger than the grid size)

8. 8
The classification process
• Create a multiband raster or stack.
• Produce training samples from known locations of desired classes.
• Develop a signature file.
• View and edit signature file if necessary.
• Run classification.

9. 9
Change Detection
• Change detection is the analysis of information about a
location over two or more points in time.
• The goals of change detection include:-
–detecting change,
–measuring the extent and magnitude of change,
–updating existing maps or GIS layers to incorporate change,
–identifying the causes of change, and
–assessing the impacts of change on environmental,
economic, and
–political conditions.

10. 10
• RS Images support all these goals and are the primary sources
of information for the first three worldwide.
• Change detection requires (like image classification):
–development of a classification scheme:
• what type of change will be detected and mapped.
–capturing the variation between the dates of images/maps that
is related to the change of interest.
–controlling all nonchange variation between the various
dates of images/maps to be compared.

11. 11
• Mapping and monitoring change can be very simple or very
complex.
• Consider the example of mapping the change caused by a
wildfire:
–Insurance: may be interested only in damage to the
structures within the fire boundary.
–the ecologist may be interested in the intensity of the fire
throughout the burned area, which will affect the soil and in
turn the ability of the area to regenerate vegetation.
• Both are interested in change caused by the wildfire:
–the type of change to be mapped needs to be defined by
classification rules

12. 12
• There are four different types of change
classification schemes:
–change/no change,
–magnitude of change,
–to/from map classes,
–causes of change.

13. 13
• Change/no change.
– This simplest change analysis provides a map of areas that have changed.
• Magnitude of change.
– Often understanding that a change has occurred is not enough.
– A forester might want to measure how the percentage of tree
cover/hectares has been decreased by clear-cuts, selective harvests,
• to/from map classes :
– Before and after map classes. With additional analysis, a change map
can be created that maps the areas of no change, and the before and
after map classes of areas that have changed.
• Causes of change.
– The most difficult change mapping involves the identification of the
causes of change.
– This can be easy when the change is easily identifiable and spectrally
significant (e.g., deforestation from clear-cuts or the impacts of many
natural disasters such as floods, wildfires, etc)