Introduction to Satellite Remote Sensing 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
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
Spectral Distance * a number that allows two measurement vectors to be compared
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 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.
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
Unsupervised ClusteringMinimum 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
Cluster center (mean) Possible pixel assignments 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 • 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 Parallelepiped • Maximum likelihood • (bayesian) • probability • Bayesian, a prior (weights) Band B Band A Minimum Distance Band B Band A
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 Thanks to: Robin Weeks
Detecting a Process: Two examples Using “band math”
Laboratory Spectral Signatures IICommon Urban Materials Healthy grass Concrete Astroturf wavelength 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
NDVI When using LANDSAT: Simple Ratio Band 3 Band 4 NDVI Band 4 - Band 3 Band 4 + Band 3 (courtesy http://earthobservatory.nasa.gov)
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) • 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
R(l) Florescence Independent of Chl-a Chl-a increasing
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
SeaWiFS empirical OC4 algorithm for Chl-a; Called a maximum-band ratio alg.