1. Image Information Extraction
GEOG3610 Remote Sensing and Image Interpretation
Image Information Extraction
Image Information Extraction

2. Image Information Extraction
GEOG3610 Remote Sensing and Image Interpretation
Image Information Extraction
Image arithmetic
Vegetation indices
Image classification
supervised classification
unsupervised classification
Image Information Extraction

3. GEOG3610 Remote Sensing and Image Interpretation
Image arithmetic
Image arithmetic uses mathematical operators on images in the similar way to those on mathematical variables.
In general: U = f(A, B)
e.g. Output = image1 + image2
The operation is on pixel-to-pixel basis without consideration of surrounding pixels.
Image Information Extraction

4. Pixel-to-pixel operation
GEOG3610 Remote Sensing and Image Interpretation
Pixel-to-pixel operation Z A B
U = f (A, B) Y U X
Image Information Extraction

5. GEOG3610 Remote Sensing and Image Interpretation
Topographic effect
The amount of illumination that a point on a surface receives is a function of the angle that the light is hitting the slope.
Lambert’s cosine law:
Image Information Extraction

6. Image showing topographic effect
GEOG3610 Remote Sensing and Image Interpretation
Image showing topographic effect
Image Information Extraction

7. GEOG3610 Remote Sensing and Image Interpretation
Band ratio
Sun
DN on slope facing sun
Unit Red NIR Red/NIR A B A B
DN on slope facing away from sun A B
Two stands of identical material will appear different if they are receiving differing amounts of illumination. The ratio between matching pixels in each band, however, will remain the same.
Image Information Extraction

8. GEOG3610 Remote Sensing and Image Interpretation
The use of band ratio
Band ratios can be used in any instances where the absorption in one band is altered in some way.
E.g. stressed/healthy vegetation
algae in turbid water
Image Information Extraction

9. Spectral band ratioing
GEOG3610 Remote Sensing and Image Interpretation
Spectral band ratioing
TM Band 2 DN = 50
TM Band 3 DN = 30
TM Band 4 DN = 135
The New DN is computed by normalizing DNration in the range of [0, 255].
Image Information Extraction

10. GEOG3610 Remote Sensing and Image Interpretation
Vegetation indices
Vegetation: absorption in visible and high reflectance in NIR.
When the vegetation become stressed, absorption decreases in visible and increases in NIR.
The higher the density of broad-leaf plants, the more distinct the difference between visible and NIR will be.
Image Information Extraction

11. Spectral bands and signatures
GEOG3610 Remote Sensing and Image Interpretation
Spectral bands and signatures
0.4
0.6
0.8
1.0
2.0
2.6
1.2
1.4
1.6
1.8
2.2
2.4 20 40 60
Reflectance (%)
Wavelength (m)
Dry bare soil (grey-brown)
Vegetation (green)
Water (clear)
SPOT VEG
Landsat ETM+
SPOT HRG
NOAA AVHRR
Image Information Extraction

12. Computing vegetation index
GEOG3610 Remote Sensing and Image Interpretation
Computing vegetation index
NIR band
Red band
VI image
Image Information Extraction

13. Some common vegetation Indices
GEOG3610 Remote Sensing and Image Interpretation
Some common vegetation Indices
Image Information Extraction

14. TM multispectral bands
GEOG3610 Remote Sensing and Image Interpretation
TM multispectral bands
TM band 3
TM band 4
Image Information Extraction

15. GEOG3610 Remote Sensing and Image Interpretation
VI images
Image Information Extraction

16. GEOG3610 Remote Sensing and Image Interpretation
Image classification
Image classification is the process of creating a meaningful digital thematic map from a image data set (information extraction).
Supervised classification: classes from known cover types.
Unsupervised classification: classes by algorithms that search the data for similar pixels.
Image Information Extraction

17. Image classification process
GEOG3610 Remote Sensing and Image Interpretation
Image classification process
What cover type is this?
It has the spectral signature like wheat, the cover type is likely to be wheat.
In this area, wheat is likely to be a farming land use.
The thematic class is therefore “farmland”.
Remotely sensed imagery
Computer decisions
Resulting class image
Image Information Extraction

18. GEOG3610 Remote Sensing and Image Interpretation
Density slice
Bmin
Bmax
Lmin
Lmax
Old pixel DN
New pixel brightness
Image Information Extraction

19. Supervised and unsupervised classification
GEOG3610 Remote Sensing and Image Interpretation
Supervised and unsupervised classification
Image data
Clustering
Seed area (example pixels)
Use clusters to define signatures or use clusters as classes
Signature information
Use a decision rule to class each pixel
Thematic image ?
Supervised classification
Unsupervised classification
Image Information Extraction

20. Supervised classification
GEOG3610 Remote Sensing and Image Interpretation
Supervised classification
Training class selection (training areas/classes)
Generating statistical parameters (spectral signatures) of training classes
Data classification
Evaluation and refinement
Image Information Extraction

21. Training class selection
GEOG3610 Remote Sensing and Image Interpretation
Training class selection
By selecting training area
Water
High buildings
Concrete
Bare soils
Forest
Image Information Extraction

22. Training class selection
GEOG3610 Remote Sensing and Image Interpretation
Training class selection
By selecting spectral range
TM Band 3 8 28 49 69 90 14 35 56 77 88
TM Band 4
Concrete
Water
Image Information Extraction

23. Supervised spectral classification
GEOG3610 Remote Sensing and Image Interpretation
Supervised spectral classification
Band 1 DNs
Band 2 DNs
Crops
Concrete
Water U
Band 1 DNs
Band 2 DNs
Crops
Concrete
Water U
Image Information Extraction

24. Parallelepiped classifier
GEOG3610 Remote Sensing and Image Interpretation
Parallelepiped classifier
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest 1 2
Image Information Extraction

25. Minimum distance classifier
GEOG3610 Remote Sensing and Image Interpretation
Minimum distance classifier
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest 1 2
Image Information Extraction

26. Maximum likelihood classifier
GEOG3610 Remote Sensing and Image Interpretation
Maximum likelihood classifier
Band x
Band y
Samples
Image Information Extraction

27. Maximum likelihood classifier
GEOG3610 Remote Sensing and Image Interpretation
Maximum likelihood classifier
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest
255
TM Band 3
TM Band 4
concrete
high buildings
grass slope
water
bare soils
forest 1 2
Image Information Extraction

28. Unsupervised classification
GEOG3610 Remote Sensing and Image Interpretation
Unsupervised classification
Cluster size
Distance between cluster means
Distance to a cluster mean
255
Band A
Band B
Clustering: use predefined parameters to identify cluster locations in data space and then to determine whether individual pixels are in those clusters or not.
Image Information Extraction

29. GEOG3610 Remote Sensing and Image Interpretation
Clustering
Band 1 DNs
Band 1 DNs
Cluster 1
Cluster 2
Cluster 3
Image Information Extraction

30. GEOG3610 Remote Sensing and Image Interpretation
Clustering process
255
Band A
Band B A
255
Band A
Band B B
A) Cluster centres are arbitrarily assigned
B) Each pixel is assigned to the nearest cluster centre in data space
Image Information Extraction

31. GEOG3610 Remote Sensing and Image Interpretation
Clustering process
255
Band A
Band B C
255
Band A
Band B D
C) The cluster means are then calculated and the pixels are reassigned to the new cluster centres
D) The process is repeated until when the cluster centres move by less than a preset distance
Image Information Extraction

32. Classification procedures
GEOG3610 Remote Sensing and Image Interpretation
Classification procedures A No
Separate data into groups with clustering
Classify data into groups
Assign name to each group
Satisfactory?
Yes No
Form images of data
Choose training pixels for each category
Calculate statistical descriptions
Satisfactory?
Yes B
Classify data into categories defined
Flow diagrams representing unsupervised classification (A) and supervised classification (B).
Image Information Extraction

33. Post-classification sorting
GEOG3610 Remote Sensing and Image Interpretation
Post-classification sorting
Purpose: "smooth" the classified image to make it look closing to a thematic map
Processes:
"Sieve" to remove isolated pixels
"Clump" to aggregate the sparsely distributed pixels into "clumps" (larger areas)
Image Information Extraction

34. GEOG3610 Remote Sensing and Image Interpretation
‘Cleaned’ images
Water
Concrete
High buildings
Bare soils
Grass slope
Forest
Image Information Extraction

35. GEOG3610 Remote Sensing and Image Interpretation
Summary
Information extraction is the key process to convert image data to spatial information.
Vegetation index (VI) uses the significant difference between red and near-infrared bands shown on the vegetation spectral signatures.
VI is a quantitative measurement but its value is only in relevant rather than absolute terms, and has no physical meanings.
Classification is used to identify and separate pixel clusters in the spectral space.
Common approaches are supervised and unsupervised classifications.
Image Information Extraction