Transcript Mapping of Coastal Wetlands via Hyperspectral AVIRIS Data

Mapping of Coastal Wetlands via
Hyperspectral AVIRIS Data
M. M. Crawford (1), A. L Neuenschwander (1),
M. J. Provancha (2)
(1) Center for Space Research, University of Texas at Austin
(2) Dynamac Corporation, Kennedy Space Center, Florida
Center for Space Research, University of Texas at Austin
CLASSIFICATION OF WETLANDS
USING AVIRIS DATA
Project Goal:
Investigate the potential of AVIRIS data for wetland
vegetation classification at the Kennedy Space Center in
Florida.
Monitor the effects of various marsh management strategies
by mapping the vegetation distribution and its change over
time.
Center for Space Research, University of Texas at Austin
OPTICAL IMAGERY OF
KENNEDY SPACE CENTER
1996 AVIRIS (Bands 49, 29, 20) of western
shore of Kennedy Space Center
1989 Landsat TM (Bands 4,3,2) of Kennedy Space Center
Center for Space Research, University of Texas at Austin
GIS MAPS OF TEST SITE
Map of Impoundments
Center for Space Research, University of Texas at Austin
Vegetation map derived from 89 TM and aerial photography
AVIRIS IMAGERY
• Airborne Visible/Infrared Imaging Spectrometer flown by
NASA’s Jet Propulsion Laboratory
• 224 Bands with 10 nm wavebands
• Measures visible and near infrared reflected energy
(400 - 2500 nm)
• Airborne Platform flown 20 km above surface
• Highly calibrated instrument
Center for Space Research, University of Texas at Austin
PRE-PROCESSING OF AVIRIS DATA
• Atmospheric correction using ATREM* (developed by
University of Colorado, CSES)
Columnar Water Vapor removed
from AVIRIS data.
Spectral Transect of “Raw” and “Corrected”
AVIRIS data.
Center for Space Research, University of Texas at Austin
FEATURE EXTRACTION
• Principal Components Analysis
• Minimum Noise Fraction (MNF) Transformation
– Orthogonal bands ordered by noise content
– Developed specifically for analysis of multi-band remotely
sensed data
• Decision Boundary Feature Extraction
Center for Space Research, University of Texas at Austin
PRINCIPAL COMPONENTS ANALYSIS
PC 1
PC 2
PC 3
PC 4
PC 5
PC 6
Center for Space Research, University of Texas at Austin
MINIMUM NOISE FRACTION (MNF)
TRANSFORMATION
MNF Band 1
MNF Band 2
MNF Band 3
MNF Band 4
MNF Band 5
MNF Band 6
Center for Space Research, University of Texas at Austin
CLASSIFICATION ALGORITHMS
INVESTIGATED
• Pixel-Based
– Gaussian Maximum Likelihood
– Neural Network: (Multi-Layered Perceptron with one hidden layer
and Scaled Conjugate Gradient Training algorithm)
– Canonical Analysis
• Region-Based
– Gaussian Markov Random Field
Center for Space Research, University of Texas at Austin
HIERARCHICAL METHODOLOGY
Level 1
AVIRIS DATA
Land
Level 2
Water
Biomass Index
Level 3
Wetlands
Uplands
Various Inputs
Various Inputs
Graminoid Marsh
Spartina Marsh
Cattail Marsh
Salt Marsh
Mud f lats
Center for Space Research, University of Texas at Austin
Scrub
Willow Sw amp
CP Hammock
CP/Oak Hammock
Slash Pine
Oak Hammock
Hardw ood Sw amp
CLASSIFIER INPUTS
• Directly compare results of input combinations using a
variety of classification algorithms
–
–
–
–
5 corrected AVIRIS Bands
First 13 eigenvectors of MNF transformation
8 eigenvectors from Principal Components Analysis
32 upland and 11 wetland features extracted from Decision
Boundary Feature Extraction (DBFE) algorithm
– 7 Upland and 5 Wetland features extracted from Canonical
Analysis (CA)
Center for Space Research, University of Texas at Austin
PIXEL-BASED CLASSIFIER RESULTS
Classifier Type
Vegetation Type
NN5
MLC5
NN-MNF13
MLC-DBFE
MLC-CA
Scrub
Willow Marsh
CP Hammock
CP/Oak Hammock
Slash Pine
Oak Hammock
Hardwood Swamp
Uplands Total
95.3
93.8
87.1
63.1
62.3
46.0
88.6
76.6
84.8
90.5
87.9
63.1
67.1
47.2
94.3
76.4
97.2
96.3
88.2
91.7
83.0
90.8
100.0
92.5
81.3
87.2
82.8
73.8
53.4
98.3
69.5
78.0
80.2
88.5
93.8
77.4
94.4
83.8
89.5
86.8
Graminoid Marsh
Spartina Bakerii Marsh
Typha Marsh
Salt Marsh
Mud Flats
Wetlands Total
74.8
87.3
83.6
97.1
92.8
87.1
74.2
89.6
90.8
96.9
73.6
85.0
98.6
90.8
94.8
99.3
83.8
93.5
80.3
77.9
75.7
88.1
79.9
80.4
79.1
83.5
82.2
87.4
82.7
83.0
Center for Space Research, University of Texas at Austin
CONTEXTUAL CLASSIFIER RESULTS
Classifier Type
Vegetation Type
MRF-PC8
MRF-MNF13
Scrub
Willow Marsh
CP Hammock
CP/Oak Hammock
Slash Pine
Oak Hammock
Hardwood Swamp
Uplands Total
93.3
90.1
89.5
74.6
94.4
96.9
96.2
90.7
93.4
93.0
93.4
83.7
78.3
92.6
98.1
90.3
Graminoid Marsh
Spartina Bakerii Marsh
Typha Marsh
Salt Marsh
Mud Flats
Wetlands Total
81.9
91.5
95.0
98.3
79.5
89.2
82.6
87.3
95.0
95.2
79.7
87.9
Center for Space Research, University of Texas at Austin
CLASSIFICATION RESULTS
Gaussian MRF using
MNF transformation
input data.
Uplands: 90.3 %
Wetlands: 87.9%
Center for Space Research, University of Texas at Austin
CLASSIFICATION RESULTS
Neural Net using
MNF transformation
input data.
Uplands: 92.5 %
Wetlands: 93.5 %
Center for Space Research, University of Texas at Austin
CLASSIFICATION RESULTS
Gaussian MLC using
5 original AVIRIS bands
as input data.
Uplands: 76.4 %
Wetlands: 85.0 %
Center for Space Research, University of Texas at Austin
CONCLUSION
• Hierarchical classification methodology was utilized
• MNF Bands used as input to classifier yielded best
results
• Gaussian Markov Random Field contextual model
classifier yielded best results
• Hyperspectral imagery is effective for classification of
coastal wetlands
Center for Space Research, University of Texas at Austin