1. Feature Extraction
“The identification of geographic features and their outlines in remote-sensing imagery through post-processing technology that enhances feature definition, often by increasing feature-to-background contrast or using pattern recognition software.
(ESRI GIS
definitions] “
Typical photo
interpretation
to digitise
features

2. data are not well understood (was) expensive classification accuracies
The extraction of features from aerial photography is the most tedious part of mapping, why hasn't Remote sensing been used more to update GIS data?
data are not well understood
(was) expensive
classification accuracies
complexities of reality
insufficient resolution

3. BC: updating of Vegetation Resource Inventory
where does it work?
Feature simplicity
Feature homogeneity
Feature certainty

5. Process for creating feature vectors from image data :
Select bands and channels to maximise feature contrast
Classify (multispectral) or threshold (single channel)
Create single DN channel or bitmap
Clean results -> sieve or filter (generalise)
RTV -> Raster to Vector conversion
smooth lines and export to GIS

6. Project example to automatically map fire polygon

8. ISODATA classification showing 50 classes
Clusters 5, 7, 12, 15, 20, 25, 37 -> burn extent
EASI modeling ->feature extraction
Sieve

9. Smoothing the pixel ‘jaggies’ …

10. Feature Points: Experiment to extract trees as points to avoid digitising – M.Sc forestry project
Green / red ratio
Threshold and sieve
Final points

11. Extracting non-forested areas near Kitimat

12. Supervised classification
Using 5,4,3 and 4/3 as input

13. Extracting glaciers and cutblocks in Kakwa
TM 3/5 ratio ->
<-TM 4/3 ratio

14. Mt. Edziza Provincial Park
Extracting current glacier extents - accumulation / ablation, compared to earlier vectors
Mt. Edziza Provincial
Park

15. Land cover boundaries

16. Image fusion to increase resolution
Landsat ETM+ 15m PAN m MS fused 15m MS
We can try this with your ALI data (10 v 30 m data) ….

18. http://www. gis. unbc. ca/courses/geog432/projects/2006/jaminf/index

19. Automatic Road Extraction from Remotely Sensed Imagery

20. Feature extraction – other applications

21. Process for creating feature vectors from image data :
Select bands and channels to maximise feature contrast – lab 4
Classify (multispectral) or threshold (single channel) - lab 3/4
Create single DN channel or bitmap – lab 3/4
Clean results -> sieve or filter (generalise) – lab 3
RTV -> Raster to Vector conversion – lab 6
smooth lines to avoid the jaggies / export to GIS – lab 6
This is also a template for possible projects – though not required

22. Kelowna, 2010

23. Urban change: Kelowna, 1999-2000