1. Introduction to Satellite Remote Sensing (continued)
Miles G. Logsdon, University of Washington, School of Oceanography,
Miles Logsdon,
Univ. of Washington
Oceanography
SeaWiFS, June 27, 2001

2. We “approach” RS in two ways
To classify or group thematic land surface materials
To detect a biophysical process

3. Cluster and Classify

4. Spectral Profile

5. Spatial Profile

6. Spectral Signatures

7. 1d classifier

8. Spectral Dimensions

9. 3 band space

10. Clusters

11. Dimensionality N = the number of bands = dimensions
…. an (n) dimensional data (feature) space
Measurement
Vector
Mean
Vector
Feature Space - 2dimensions
190 85
Band B
Band A

12. Spectral Distance * a number that allows two measurement vectors to be
compared

13. Classification Approaches
Unsupervised: self organizing in multi-dimensions
Easy, quick, something you should be able to do
Supervised: training with spectral signatures
More thought, planning ahead, for target application
Hybrid: self organization by categories
Just what it implies
Spectral Mixture Analysis: sub-pixel variations
Seeks the composition of the pixel signatures

14. Clustering / Classification
Clustering or Training Stage:
Through actions of either the analyst’s supervision or an unsupervised algorithm, a numeric description of the spectral attribute of each “class” is determined (a multi-spectral cluster mean signature).
Classification Stage:
By comparing the spectral signature to of a pixel (the measure signature) to the each cluster signature a pixel is assigned to a category or class.

15. terms
Parametric = based upon statistical parameters (mean & standard deviation)
Non-Parametric = based upon objects (polygons) in feature space
Decision Rules = rules for sorting pixels into classes

16. Unsupervised Clustering Minimum Spectral Distance
ISODATA
I - iterative
S - self
O - organizing
D - data
A - analysis
T - technique
A - (application)?
Band B
Band A
Band B
Band A
1st iteration cluster mean
2nd iteration cluster mean

17. ISODATA clusters

18. Part of the classification process: ISODATA clustering algorithm
Band 4
Possible pixel
assignments
Band 3
Cluster center (mean)
Each data pixel is
assigned to a cluster based on the distance of a pixel from the center of a cluster (“Euclidean distance”)

19. Supervised Classification Assigning spectral signatures to clusters by selecting pixel in “geographic space”

20. Supervised Classification Assigning spectral signatures to clusters by selecting pixel in “Feature space”

21. Classification Decision Rules
If the non-parametric test results in one unique class, the pixel will be assigned to that class.
if the non-parametric test results in zero classes (outside the decision boundaries) the the “unclassified rule applies … either left unclassified or classified by the parametric rule
if the pixel falls into more than one class the overlap rule applies … left unclassified, use the parametric rule, or processing order
Non-Parametric
parallelepiped
feature space
Unclassified Options
parametric rule
unclassified
Overlap Options
by order
Parametric
minimum distance
Mahalanobis distance
maximum likelihood

22. Parallelepiped Maximum likelihood (bayesian) Minimum Distance
probability
Bayesian, a prior (weights)
Band B
Band A
Minimum Distance
Band B
Band A
cluster mean
Candidate pixel

23. Parametric classifiers

24. Class Names or “Classification Systems”
USGS - U.S. Geological Survey Land Cover Classification Scheme for Remote Sensor Data
USFW - U.S. Fish & Wildlife Wetland Classification System
NOAA CCAP - C-CAP Landcover Classification System, and Definitions
NOAA CCAP - C-CAP Wetland Classification Scheme Definitions
PRISM - PRISM General Landcover
King Co. - King County General Landcover (specific use, by Chris Pyle)
Level
1 Urban or Built-Up Land
11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications and Utilities
15 Industrial and Commercial Complexes
16 Mixed Urban or Built-Up
17 Other Urban or Built-up Land
2 Agricultural Land
21 Cropland and Pasture
22 Orchards, Groves, Vineyards, Nurseries and Ornamental Horticultural Areas
23 Confined Feeding Operations
24 Other Agricultural Land

25. Resolution and Spectral Mixing
Thanks to:
Robin Weeks

27. Detecting a Process: Two examples
Using “band math”

28. Laboratory Spectral Signatures II Common Urban Materials
Healthy grass
Concrete
Astroturf
wavelength
Thanks to Robin Weeks

29. Vegetation: Pigment in Plant Leaves (Chlorophyll) strongly absorbs visible light (0.4 to 0.7 μm) Cell Structure however strongly reflects Near-IR (0.7 – 1.1 μm)
Thanks to Robin Weeks

30. (courtesy http://earthobservatory.nasa.gov)
NDVI
When using LANDSAT:
Simple Ratio
Band 3
Band 4
NDVI
Band 4 - Band 3
Band 4 + Band 3
(courtesy

31. Ocean Color
Let’s begin with phytoplankton
Phyton = plant; planktos = wandering.
These reproduce asexually, are globally distributed, consist of 10s of thousands of species and make up about 25% of the total planetary veg.
These are the grass that the zooplankton graze upon.
And, they fix carbon as well.

32. Chaetoceros species of diatoms: cells are 20-25 mm in diameter.
Chloroplasts contain pigments
Chaetoceros species of diatoms: cells are mm in diameter.

33. Water provides an internal standard shape for spectral comparison with other variable components
Slopes for pigments and CDOM similar from 440 to 600 nm, but are opposite from 400 to 440 nm
Note that detritus is include with CDOM since shapes are similar
Spectral de-convolution of pigment absorption from CDOM absorption is straight-forward
Shapes of phytoplankton or pigment absorption are not constant (next slide)
For Case 2 waters, ratio of CDOM to chlorophyll a is not constant
Strategy for Spectral Separation of Absorption Components with Semi-Analytic Algorithm
Ken Carder: University of South Florida

34. Colored Dissolved Organic Material (CDOM)
Organic Sources
Terrestrial CDOM
decay vegetation from river and nearshore
Ocean CDOM
detritus - cell fragments, zooplankton fecal
Inorganic Sources
Sand & Dust => Errosion
rivers, wind, wave or current suspension

35. What’s the difference between MODIS chlorophylls?
“Case 1” waters: Chlor_MODIS (Clark) This is an empirical algorithm based on a statistical regression between chlorophyll and radiance ratios.
“Case 2” waters: Chlor_a_3 (Carder) This is a semi-analytic (model-based) inversion algorithm. This approach is required in optically complex “case 2” (coastal) waters and low-light, nutrient-rich regions (hi-lats).
A 3rd algorithm was added to provide a more direct linkage to the SeaWiFS chlorophyll:
“SeaWiFS-analog” Chlor_a_2 (Campbell)
SeaWiFS algorithm OC4.v4 (O’Reilly)
Ken Carder: University of South Florida

36. R(l)
Florescence
Independent of Chl-a
Chl-a increasing

37. Case 1 Rrs Model with superimposed MODIS bands 8-14: All variables co-vary with chlorophyll a
Note that slopes between blue and green wave lengths decrease with increasing chlorophyll, explaining the strategy of empirical algorithms
Case 2 waters are more complicated
Ken Carder: University of South Florida

39. SeaWiFS empirical OC4 algorithm for Chl-a; Called a maximum-band ratio alg.

41. Flying