spectral angel mapper (sam) algorithm for landuse mapping

Spectral Angel Mapper (SAM) Algorithm for Landuse Mapping

Partha Pratim GhoshProduct Specialist, ESRI India

Dr. Deb Jyoti PalVice President, ESRI India

Dr. Pabitra BanikProfessor, Indian Statistical Institute, Kolkata

Dr. Nilanchal PatelProfessor & 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)

Image

Landsat ETM+ ImagePurulia District, West Bengal

India

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

SupervisedParallelpipedMinimum DistanceMaximum LikelihoodMahalanobis Distance

UnsupervisedIsoDataK-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 1Vegetation

Class 3Water

Class 2Urban

Water

Vegetation

Urban

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 SpectraLaboratory 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.


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 v1

Tv2

v1 v2


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)


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



n-Dimensional Visualizer (n-D)


Creation of Unidentified Spectral Library

Classification using SAM

Class Identification


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


•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.


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


Surveyed villages and markets in Purulia District

District marketMajor marketsMinor marketsRailwayRoadBlock Boundary

Surveyed villages

Legend:

Data source: ISI, Calcutta, India


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

Discrepancy (%) 3.3 -6.6 0.2 15.12 -6.1 9.8Figure 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

27

1. Natural Resource Inventory of Luppi Village, Eastern Plateau of India: Implications for Sustainable Agricultural Development by P. Banik, Ashim Midhya, Sharon Fajardo, P. Suan Kam ---- Journal of Sustainable Agriculture, Volume 28, Issue 2, Page 85 – 100.

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References

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Thank You

spectral angel mapper (sam) algorithm for landuse mapping

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