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Introduction to Satellite Remote Sensing (continued). Miles Logsdon, Univ. of Washington Oceanography. SeaWiFS, June 27, 2001. We “approach” RS in two ways. To classify or group thematic land surface materials To detect a biophysical process. Cluster and Classify. Spectral Profile.

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introduction to satellite remote sensing continued
Introduction to Satellite Remote Sensing(continued)

Miles Logsdon,

Univ. of Washington


SeaWiFS, June 27, 2001

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

N = the number of bands = dimensions

…. an (n) dimensional data (feature) space





Feature Space - 2dimensions



Band B

Band A

spectral distance
Spectral Distance

* a number that allows two measurement vectors to be


classification approaches
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
clustering classification
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.
  • 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
unsupervised clustering minimum spectral distance
Unsupervised ClusteringMinimum Spectral Distance


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


Cluster center (mean)

Possible pixel


Band 4

Band 3

Part of the classification process: ISODATA clustering algorithm

Each data pixel is

assigned to a cluster based on the distance of a pixel from the center of a cluster (“Euclidean distance”)

Supervised ClassificationAssigning spectral signatures to clusters by selecting pixel in “geographic space”

Supervised ClassificationAssigning spectral signatures to clusters by selecting pixel in “Feature space”

classification decision rules
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
  • parametric rule
  • by order
  • unclassified
  • Parametric
  • minimum distance
  • Mahalanobis distance
  • maximum likelihood

cluster mean

Candidate pixel


  • Maximum likelihood
  • (bayesian)
  • probability
  • Bayesian, a prior (weights)

Band B

Band A

Minimum Distance

Band B

Band A

class names or classification systems
Class Namesor“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
resolution and spectral mixing
Resolution and Spectral Mixing

Thanks to:

Robin Weeks

laboratory spectral signatures ii common urban materials
Laboratory Spectral Signatures IICommon Urban Materials

Healthy grass




Thanks to Robin Weeks


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



When using LANDSAT:

Simple Ratio

Band 3

Band 4


Band 4 - Band 3

Band 4 + Band 3


ocean color
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.

Chloroplasts contain pigments

Chaetoceros species of diatoms: cells are 20-25 mm in diameter.


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

colored dissolved organic material cdom
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

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




Independent of Chl-a

Chl-a increasing

case 1 rrs model with superimposed modis bands 8 14 all variables co vary with chlorophyll a
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