1. 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

2. 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.

3. 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

4. GIS MAPS OF TEST SITE Map of Impoundments
Vegetation map derived from 89 TM and aerial photography

5. 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
( nm)
Airborne Platform flown 20 km above surface
Highly calibrated instrument

6. 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.

7. 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

8. PRINCIPAL COMPONENTS ANALYSIS
PC 1
PC 2
PC 3
PC 4
PC 5
PC 6

9. MINIMUM NOISE FRACTION (MNF) TRANSFORMATION
MNF Band 1
MNF Band 2
MNF Band 3
MNF Band 4
MNF Band 5
MNF Band 6

10. 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

11. HIERARCHICAL METHODOLOGY
Level 1
Level 2
Level 3

12. 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)

13. PIXEL-BASED CLASSIFIER RESULTS
NN5 MLC5 NN-MNF MLC-DBFE MLC-CA
Vegetation Type
Classifier Type
Scrub
Willow Marsh
CP Hammock
CP/Oak Hammock
Slash Pine
Oak Hammock
Hardwood Swamp
Uplands Total
Graminoid Marsh
Spartina Bakerii Marsh
Typha Marsh
Salt Marsh
Mud Flats
Wetlands Total

14. CONTEXTUAL CLASSIFIER RESULTS
MRF-PC8 MRF-MNF13
Classifier Type
Vegetation Type
Scrub
Willow Marsh
CP Hammock
CP/Oak Hammock
Slash Pine
Oak Hammock
Hardwood Swamp
Uplands Total
Graminoid Marsh
Spartina Bakerii Marsh
Typha Marsh
Salt Marsh
Mud Flats
Wetlands Total

15. CLASSIFICATION RESULTS
Gaussian MRF using
MNF transformation
input data.
Uplands: 90.3 %
Wetlands: 87.9%

16. CLASSIFICATION RESULTS
Neural Net using
MNF transformation
input data.
Uplands: %
Wetlands: %

17. CLASSIFICATION RESULTS
Gaussian MLC using
5 original AVIRIS bands
as input data.
Uplands: 76.4 %
Wetlands: 85.0 %

18. 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