Transcript Change Detection - Center for Land Use Education and Research

A Comparison of Land Use and Land
Cover Change Detection Methods
Daniel L. Civco, James D. Hurd, Emily H. Wilson,
Mingjun Song, Zhenkui Zhang
Center for Land use Education And Research
Department of Natural Resources Management & Engineering
The University of Connecticut
U-4087, Room 308, 1376 Storrs Road
Storrs, CT 06269-4087
Outline
•
•
•
•
Background
Objectives
Study Area and Data
Methods
–
–
–
–
Post Classification Analysis
Cross-correlation Analysis
Neural Networks
Segmentation & Object-oriented Classification
• Results
• Conclusions
• Recommendations
Northeast
Applications of
Useable
Technology
In
Land planning for
Urban
Sprawl
A NASA Regional
Earth Science
Applications Center
(RESAC)
Our RESAC Mission
To make the power of remote sensing
technology available, accessible and
useable to local land use decision makers
as they plan their communities.
To educate the general public on the value
and utility of geospatial technologies,
particularly RS information.
NAUTILUS Research
Better land cover mapping and change
detection
Urban growth models and metrics
Forest fragmentation models
and metrics
Improved impervious
cover estimates
Background
• Need for effective methods for deriving
information on
– Land use change
– Forest fragmentation
– Urban growth
– Loss of agricultural lands
– Increase in impervious surface area
Background
Farmland conversion in the Stony Brook Millstone Watershed: 1995-1999
Genesis 3D rendering of land use derived from Landsat data
Objective
Compare the results of different land
use and land cover change detection
approaches
• traditional post-classification
•
•
•
•
cross-tabulation
cross-correlation analysis
neural networks
knowledge-based expert systems
image segmentation & objectoriented classification
Research & Education Watersheds
Presumpscot
SuAsCo
Salmon
Stonybrook
A range of land
covers and issues
Stony Brook Millstone
Watershed, NJ
• Has a strong Watershed
Association in existence
• Between New York City and
Philadelphia
• Increased development pressures
• Loss of agriculture land to
urban sprawl
USGS MRLC
265 Sq. Miles
Study Area and Data
SITE 1
SITE 2
Stony Brook
Millstone Watershed
Study Area and Data
March 27, 1989
Site 1
May 4, 2000
Study Area and Data
September 3, 1989
Site 1
September 23, 1999
Study Area and Data
March 27, 1989
Site 2
May 4, 2000
Study Area and Data
September 3, 1989
Site 2
September 23, 1999
Study Area and Data
September 3, 1989
September 23, 1999
Methods
– Post Classification Analysis
– Cross-correlation Analysis
– Neural Networks
– Segmentation & Object-oriented
Classification
Post Classification Analysis
1989 Classification Iteration 1
• Step 1
– Unsupervised
classification
• Identify known
clusters
• Extract unknown
clusters
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Unknown
Post Classification Analysis
1989 Classification Iteration 2
• Step 2
– Unsupervised
classification
• Identify known
clusters
• Extract unknown
clusters
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Unknown
Post Classification Analysis
1989 Classification Iteration 3
• Step 3
– Unsupervised
classification
• Identify known
clusters
• Extract unknown
clusters
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Unknown
Post Classification Analysis
1989 Classification Iteration 4
• Step 4
– Unsupervised
classification
• Identify clusters
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Unknown
Post Classification Analysis
• Step 5
– Combine
iterations into
single land cover
image
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Post Classification Analysis
• Step 6
– Smooth image
using majority
filters
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Post Classification Analysis
Perform similar procedure on 2000 date
1989 Classification
Site 1
2000 Classification
Post Classification Analysis
1989 Classification
Site 2
2000 Classification
Post Classification Analysis
1989 Classification
2000 Classification
Cross Tabulate
Site 2
Change
Methods
– Post Classification Analysis
– Cross-correlation Analysis
– Neural Networks
– Segmentation & Object-oriented
Classification
Cross-correlation Analysis
CCA calculates the sum of the distance of
each pixel in each band from the norm
 Observedi  Expectedi 

Z   
Std.Dev.i
i 1 

n
•
•
•
•
2
Z is the distance measure
Observed is the pixel value for each band
Expected is the mean value of all extracted pixels for each band
Std. Dev. Is the standard deviation of all extracted pixels for each band
Cross-correlation Analysis
1989 Deciduous Category
• Step 1
– Use 1989
classification as
base land cover
– Extract vegetated
class areas to be
analyzed from
1999/2000 ETM image
(turf & grass, agriculture &
barren, deciduous, and
coniferous)
September 23, 1999
Cross-correlation Analysis
1989 Deciduous Category
• Step 2
– Perform CCA on
1999/2000
imagery
– Identify
thresholds
separating
unchanged
pixels and
changed pixels
Probable
unchanged
Probable
changed
Z-values range from
1 to 5,794
Cross-correlation Analysis
• Step 3
– Create a mask
from changed
pixels for all
categories
analyzed
(turf & grass, agriculture
& barren, deciduous,
and coniferous)
Turf & Grass
Agriculture &
Barren
Deciduous
Coniferous
Cross-correlation Analysis
• Step 4
– Extract changed
pixels from
1999/2000 image
data
– Perform
unspervised
classification to
identify new
categories
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Cross-correlation Analysis
• Step 4
– Merge new
classes with
historic
classification to
produce updated
land cover
Dense
Urban
Residential
Turf &
Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Cross-correlation Analysis
1989 Classification
Site 1
2000 Classification
Cross-correlation Analysis
1989 Classification
Site 2
2000 Classification
Methods
– Post Classification Analysis
– Cross-correlation Analysis
– Neural Networks
– Segmentation & Object-oriented
Classification
Neural Networks
Nautilus
Image
Processing
System
Neural Networks
• Step 1
– Select training
features based
on points
Neural Networks
• Step 2
– Extract digital
numbers for:
• Each pixel
– By class
• Each Band
Neural Networks
Records
259
Features
7
Classes
9
Band 1
Class actually represented
by one-of-n encoding. (i.e.,
0 0 0 1 0 0 0 0 0
Band 2
Band 3
Band 4
Band 5
Band 7
Band 6
Class
69
28
22
151
103
129
30
4
65
26
21
167
107
129
31
4
….
….
….
….
….
….
….
….
77
35
42
66
77
135
41
9
79
34
46
69
82
135
43
9
….
….
….
….
….
….
….
….
61
20
15
83
53
124
13
5
60
20
15
83
50
124
12
5
….
….
….
….
….
….
….
….
Example of Site 2 Training Data for September 23, 1999
Neural Networks
Step 3: Create data set of all possible from T1 to T2
changes (constrained by permitted changes)
Neural Networks
FromTo
Urban
Residntl
Urban
Residential
Turf&Grass
Agriculture
Deciduous
Coniferous
Water
Wetland
Barren
Permitted Changes
Turf&Grass
Agric
Decid
Conif
Water
Wetland
Barren
Neural Networks
• Step 4
– Create neural
network classifier
• NeuralSIM®
– Backpropagation
– Export C-code
– Compile into
NIPS
Neural Networks
• Step 5
– Perform full
neural networkbased change
detection within
NIPS
Methods
– Post Classification Analysis
– Cross-correlation Analysis
– Neural Networks
– Segmentation & Object-oriented
Classification
Segmentation and Objectoriented Classification
• Step 1:
Preprocessing
– Prepare Image Data (2
dates and 2 seasons = 4
images)
– Use indices to extract
obvious classes
– Create a data layer using
the knowledge engineer
– Add to image data for
input into eCognition
Segmentation and Objectoriented Classification
• Step 1:
Preprocessing
– Prepare Image Data (2
dates and 2 seasons = 4
images)
– Use indices to extract
obvious classes
– Create a data layer using
the knowledge engineer
– Add to image data for
input into eCognition
Date 1
Spring
NDVI
Date 1
Spring
NDMI
Date 2
Spring
NDVI
Date 1
Summer
NDVI
Date 1
Summer
NDMI
Date 2
Spring
NDMI
Date 2
Summer
NDVI
Date 2
Summer
NDMI
Segmentation and Objectoriented Classification
• Step 1:
Preprocessing
– Prepare Image Data (2
dates and 2 seasons = 4
images)
– Use indices to extract
obvious classes
– Create a data layer using
the knowledge engineer
– Add to image data for
input into eCognition
Segmentation and Objectoriented Classification
• Step 1:
Preprocessing
– Prepare Image Data (2
dates and 2 seasons = 4
images)
– Use indices to extract
obvious classes
– Create a data layer using
the knowledge engineer
– Add to image data for
input into eCognition
Site 1
Site 2
Segmentation and Objectoriented Classification
• Step 2
– Create eCognition
Projects for date 1
and date 2
– Segment images
using both seasons
of imagery
(excluding layer 7)
• Input Data: 7 layers
•
•
•
•
•
•
•
1. Red (spring)
2. NIR (spring)
3. MIR (spring)
4. Red (summer)
5. NIR (summer)
6. MIR (summer)
7. Classified layer
Segmentation and Objectoriented Classification
• Step 2
– Create eCognition
Projects for date 1
and date 2
– Segment images
using both seasons
of imagery
(excluding layer 7)
• Segment four levels
Level
1
2
Scale
Color
Shape
3
1.0
0.0
5
0.8
0.2
3
10
0.7
0.3
4
20
0.5
0.5
Segmentation and Objectoriented Classification
• Step 2
– Create eCognition
Projects for date 1
and date 2
– Segment images
using both seasons
of imagery
(excluding layer 7)
Pixel 4
Level
1
2
3
Segmentation and Objectoriented Classification
• Step 3
– Begin creating class
hierarchy
– Training segment
selection and
standard nearest
neighbor
– Nearest Neighbor
Classification of
each level
Segmentation and Objectoriented Classification
• Step 3
– Begin creating class
hierarchy
– Training segment
selection and
standard nearest
neighbor
– Nearest Neighbor
Classification of
each level
Segmentation and Objectoriented Classification
• Step 3
– Begin creating class
hierarchy
– Training segment
selection and
standard nearest
neighbor
– Nearest Neighbor
Classification of
each level
Segmentation and Objectoriented Classification
• Step 3
– Begin creating class
hierarchy
– Training segment
selection and
standard nearest
neighbor
– Nearest Neighbor
Classification of
each level
Segmentation and Objectoriented Classification
• Step 4
– Adding knowledge
to each eCognition
project
– Refinement and final
classification with
class-related
features
Utilize other
the layer
spatial
levels
attributes
7 mask
Water in
The classified
Level 1 is
layer based
must
Turf and
be
solely
classon
3Grass
the must
existence
border of
water
residential
in
or
Level
other3turf
and grass
Water in Level 3
is based on the
summer red band
(layer 4) and the
standard nearest
neighbor samples
Turf and
Grass must
have
residential in
level 3
Segmentation and Objectoriented Classification
• Step 4
– Adding knowledge
to each eCognition
project
– Refinement and final
classification with
class-related
features
Site 2,
1, Date 2
1
Segmentation and Objectoriented Classification
• Step 5
– Use the knowledgebased classifier in
ERDAS Imagine to
do a postclassification
change detection
– Final change
classifications for
Site 1 and Site 2
Segmentation and Objectoriented Classification
• Step 5
– Use the knowledgebased classifier in
ERDAS Imagine to
do a postclassification
change detection
– Final change
classifications for
Site 1 and Site 2
Site 2
1
Results
– Post Classification Analysis
– Cross-correlation Analysis
– Neural Networks
– Segmentation & Object-oriented
Classification
Post Classification Analysis
Site 1
Urban
Agriculture
Forest
Site 2
Water
Barren
Agr to
Urban
Forest to
Urban
Barren to
Urban
Cross-correlation Analysis
Site 1
Urban
Agriculture
Forest
Site 2
Water
Barren
Agr to
Urban
Forest to
Urban
Barren to
Urban
Neural Networks
Site 1
Urban
Agriculture
Forest
Site 2
Water
Barren
Agr to
Urban
Forest to
Urban
Barren to
Urban
Segmentation and Object-oriented
Classification
Site 1
Urban
Agriculture
Forest
Site 2
Water
Barren
Agr to
Urban
Forest to
Urban
Barren to
Urban
September 3, 1989
Post-classification
Change Detection
September 23, 1999
Agriculture
To
Urban
Forest
To
Urban
Barren
To
Urban
September 3, 1989
Cross-Correlation
Change Detection
September 23, 1999
Agriculture
To
Urban
Forest
To
Urban
Barren
To
Urban
September 3, 1989
Neural Network
Change Detection
September 23, 1999
Agriculture
To
Urban
Forest
To
Urban
Barren
To
Urban
September 3, 1989
Object-oriented
Change Detection
September 23, 1999
Agriculture
To
Urban
Forest
To
Urban
Barren
To
Urban
Conclusions
• The results of this research reveal that there
is merit to each of the several land use
change detection methods studied, but that
there appears to be no single best way in
which to perform change analysis
• The most significant conclusion of this study
is that much research remains to be done
to improve upon the results of land use and
land cover change detection
Recommendations
• These investigators firmly believe that an
approach based on image-segmentation
and rule-based classification is potentially
such an improved methodology, and
accordingly intend on pursuing the avenues
of neural network and object-oriented
classification change detection, perhaps in an
integrated approach.
Acknowledgement
National Aeronautics and Space Administration
Grant NAG13-99001/NRA-98-OES-08 RESACNAUTILUS, Better Land Use Planning for the
Urbanizing Northeast: Creating a Network of ValueAdded Geospatial Information, Tools, and Education
for Land Use Decision Makers.
Northeast Applications of Useable Technology In Land planning for Urban Sprawl
This presentation
is available at
resac.uconn.edu
A Comparison of Land Use and Land
Cover Change Detection Methods
Daniel L. Civco, James D. Hurd, Emily H. Wilson,
Mingjun Song, Zhenkui Zhang
Center for Land use Education And Research
Department of Natural Resources Management & Engineering
The University of Connecticut
U-4087, Room 308, 1376 Storrs Road
Storrs, CT 06269-4087