Transcript SAM for Land use Mapping

Spectral Angel Mapper (SAM) Algorithm
for Landuse Mapping
Partha Pratim Ghosh
Product Specialist, ESRI India
Dr. Deb Jyoti Pal
Vice President, ESRI India
Dr. Pabitra Banik
Professor, Indian Statistical Institute, Kolkata
Dr. Nilanchal Patel
Professor & Head, Birla Institute of Technology, Ranchi
Agenda
Hypothesis
Research Question
Advantages of Spectral Angel Mapper (SAM)
Minimum Noise Fraction (MNF)
Pixel Purity Index (PPI)
n-Dimensional Visualizer (n-D)
Endmember Collection
Classification
Result
Conclusion
Hypothesis
Land use and land management practices have a
major impact on natural resources including water,
soil, nutrients, plants and animals. Accurate Land use
information must be develop for accurate policy
making.
Digital Image classification is one of the well
accepted method to extract Land use information
system and many limitation like mixed pixel and noise
issues has been observed in the conventional pixel
based classification techniques.
Research Question
1. Can we use spectra based classification techniques
for multispectral image to develop an accurate land
use information system?
2. Can we overcome the issues related to mixed pixel
specifically observed in case of different type of
vegetation?
3. Can we identify crops using spectra?
Study Area
Eastern Part of Eastern Plateau Area
(Purulia District of West Bengal)
Landsat ETM+ Image
Purulia District, West Bengal
India
Image
Landcover
Land cover is the physical
material at the surface of
the earth, which naturally
cover the earth surface.
e.g. grass, asphalt, trees,
bare ground, water, etc..
Landuse
“The arrangements, activities
and inputs people undertake
in a certain land cover type to
produce, change or maintain
it is called land use" (FAO,
1997a; FAO/UNEP, 1999).
Land
use
involves
the
management and modification
of natural environment or
wilderness
into
built
environment such as fields,
pastures, and settlements.
LULC Mapping Techniques
•Survey
•Pixel based Satellite Image Classification using
Remote Sensing Software
Supervised
Parallelpiped
Minimum Distance
Maximum Likelihood
Mahalanobis Distance
Unsupervised
IsoData
K-Means
•Spectra based
Spectral Angel Mapper (SAM)
Spectral Information Divergence (SID)
Limitation of Pixel Based
Classification
Each pixel in the image is compared to the training site
signatures identified by the analyst and labeled as the class
it most closely "resembles" digitally.
Class 1
Vegetation
Urban
Water
Class 2
Urban
Vegetation
Class 3
Water
Spectral Angel Mapper (SAM)
SAM is an automated method for comparing image spectra to
individual spectra or to a spectral library (Boardman,
unpublished data; CSES, 1992; Kruse et al., 1993a).
Image Spectra
Laboratory Spectra
Image Spectra & Laboratory Spectra are matching
The algorithm determines the similarity between two spectra
by calculating the spectral angle between them, treating them
as vectors in n-D space, where n is the number of bands.
Spectral Angel Mapper (SAM)
In a two-dimensional feature space
defined by bands x and y, two
spectral signatures that represent two
different surface objects can be
represented as vectors v1, and v2.
Then the spectral distance (Euclidean
distance) is the length of the line
segment d connecting the end points
of the two vectors v1 and v2. The
spectral angle is the angle between
the two vectors v1 , and v2 : i.e.,
θ (v1, v2)=Cos -1
v1Tv2
v1 v2
Spectral Angel Mapper (SAM)
If we linearly scale the length of vectors
v1 and v2, by distance r, the spectral
distance will be scaled by r.
On the other hand the cosine of the
angle θ between the two vectors v1 and
v2, remains the same.
Because of this invariant nature of the
cosine of the angle θ
to the linearly
scaled variations, it becomes sensitive to
the shape of the spectral patterns. Sohn
et al. (1999)
Spectral Angel Mapper (SAM)
Small spectral angel (Cos θ) between the two spectrums
indicate high similarity and high angles indicate low similarity.
The spectra of the same type of surface objects are
approximately linearly scaled variations of one another due to the
atmospheric and topographic variations. So the actual vectors in
feature space will fall slightly above or below the linearly scaled
vectors. But the changes in the cosine of the angle θ caused by
these variations remain very small (Sohn et al., 1999).
Method
Atmospheric Correction of Image
Minimum Noise Fraction (MNF)
Pixel Purity Index (PPI)
n-Dimensional Visualizer (n-D)
Endmember Collection
Creation of Unidentified Spectral Library
Classification using SAM
Class Identification
Minimum Noise Fraction (MNF)
MNF transform is used to segregate noise in the data, and to reduce the
computational requirements for subsequent processing (Boardman and
Kruse, 1994). The MNF transform as modified from Green et al. (1988) and
used in ENVI
Pixel Purity Index (PPI)
•The Pixel Purity Index (PPI) is a means of finding the most “spectrally pure,”
or extreme, pixels in multispectral and hyperspectral images (Boardman et al.,
1995).
•The Pixel Purity Index records the total number of times each pixel is marked
as extreme. A "Pixel Purity Image" is created in which the DN of each pixel
corresponds to the number of times that pixel was recorded as extreme.
n-Dimensional Visualizer
Spectra can be thought of as points in an n -dimensional scatter plot,
where n is the number of bands.
The n-D Visualizer help to visualize the shape of a data cloud that results
from plotting image data in spectral space (with image bands as plot axes).
We typically used the n-D Visualizer with spatially subsetted Minimum
Noise Fraction (MNF) data that use only the purest pixels determined from
the Pixel Purity Index (PPI).
.
n-Dimensional Visualizer
Rotating n-D Visualizer interactively
we can select groups of pixels in
classes. Selected classes can be
exported to us in the classification.
n-D Visualizer can be used to check
the separability of the classes when
the regions of interest (ROIs) as input
into supervised .
The n-D Visualizer is an interactive
tool to use for selecting the
endmembers in n-D space.
Endmember Collection
Endmembers are spectra that are chosen to represent pure
surface materials in a spectral image. Endmembers that
represent radiance or reflectance spectra must satisfy a positivity
constraint (containing no values less than zero).
SAM Classification
Use the Endmember in Spectral Angel Mapper Algorithm
Class Identification
Surveyed villages and markets in Purulia District
Legend:
Surveyed villages
District market
Major markets
Minor markets
Railway
Road
Block Boundary
Data source: ISI, Calcutta, India
Class Identification
Landuse Ecosystem Pattern of Kashipur Block
Result
Jhalda I
Kashipur
Manbazar I
Manbazar II
Purulia I
Purulia II
Forest
5125 (15.7)
303 (0.7)
1427 (3.7)
958 (3.9)
785 (3.0)
252 (0.7)
Degraded Forest
8735 (26.8)
3346 (8.1)
7751 (20.3)
4965 (20.46)
1119 (4.2)
2169 (6.4)
Tanr (Upper terrace)
1832 (5.6)
2737 (6.6)
6915 (18.1)
3935 (16.22)
4975 (18.8)
3617 (10.6)
Mid Terrace
7613 (23.4)
17152 (41.5)
9609 (25.2)
5671 (23.37)
9132 (34.6)
14920 (43.8)
Lower Terrece
5770 (17.7)
11709 (28.3)
10416 (27.3)
4526 (18.65)
8470 (32.1)
10982 (32.3)
Stream Channel
2677 (8.2)
5158 (12.5)
774 (2.0)
683 (2.81)
929 (3.5)
1343 (3.9)
Reservoir and Ponds
675 (2.1)
448 (1.1)
1059 (2.8)
2480 (10.22)
536 (2.0)
756 (2.2)
Sand bank
-
479 (1.2)
241 (0.6)
39 (0.2)
140 (0.5)
-
Town area
125 (0.4)
-
-
-
333 (1.3)
-
Total area (computed)
32553
41331
38192
24257
26420
34039
Total area (2001 Census)
31509
44252
38132
28581
28150
31011
3.3
-6.6
0.2
15.12
-6.1
9.8
Discrepancy (%)
Figure within parenthesis is in percent
Conclusion
1. Even if Landsat ETM+ is a medium spatial
resolution and that sub-pixel contamination cover
material is evident while selecting endmembers, it
has given good results in SAM.
2. The classification map generated with SAM for Landsat
ETM+ show that this method could effectively be used for
landuse mapping.
3. With the help of MNF, PPI & n-D Visualizer the
mixed pixel issue can be addressed up to certain
level
References
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Thank You
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