artificial intelligence for mixed pixel resolution
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Artificial Intelligence For Mixed Pixel Resolution. By Nitish Gupta (Guru Gobind Singh Indraprastha University) Dr. V.K. Panchal - PowerPoint PPT PresentationTRANSCRIPT
Artificial Intelligence For Mixed Pixel Resolution
By
Nitish Gupta (Guru Gobind Singh Indraprastha University)
Dr. V.K. Panchal (Defence Research Development Organization)
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OutlineOutline
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Conflicts are one of the most characteristic attributes in Satellite Remote Sensing multilayer imagery.
Class conflict occurs when there is presence of spectrally indiscernible distinct classes and how the human experts understand it based on his/her expertise.
Can we resolve those mixed pixels ?
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SPATIAL RESOLUTION & MIXED PIXEL
100Meter resolutionPatalganga, India
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SPATIAL RESOLUTION & MIXED PIXEL
5 Meter resolutionPatalganga, India
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SPATIAL RESOLUTION & MIXED PIXEL
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1. Mixed pixel due to the presence of small, sub-pixel targets within the area it represents .
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SPATIAL RESOLUTION & MIXED PIXEL
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2. Mixing as a result of the pixel straddling the boundary of discrete thematic classes .
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3. Mixing due to gradual transition observed between continuous thematic classes .
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Aral Sea
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4. Mixing problem due to the contribution of a target (black spot) outside the area represented by a pure but influenced by its point spread function.
So, Mixed Pixels are major concern in satellite image classification !!
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When two distinct objects display
similar spectral signatures / Fingerprints
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Nature is a Powerful Paradigm
We can learn from nature.
Study of the geographical distribution of biological organisms.
Species migrate between “islands” via flotsam, wind, flying, swimming, …
Habitat Suitability Index (HSI): Some islands are more suitable for habitation than others.
Suitability Index Variables (SIVs): Habitability is related to features such as rainfall, topography, diversity of vegetation, temperature, etc.
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1.Initialize a set of solutions to a problem.
2. Compute “fitness” (HSI) for each solution.
3. Compute S, λ, and μ for each solution.
4. Modify habitats (migration) based on λ, μ.
5. Mutation based on probability.
6. Choose the best candidate & go to step 2 for the next iteration if needed.
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TERRAIN FEATURES
RADIO SPECTROMETER
SPECTRAL SIGNATURES
BIO-GEOGRAPHY BASEDOPTIMIZATION
DOMAIN EXPERT
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MIXED PIXEL RESOLVED
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ANALYSING MULTISPECTRAL IMAGE OF ALWAR (RAJASTHAN, INDIA)
False Color Composition Image
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Image Dimension - 476X572 Pixels.
Image’s spectral Bands- LISS-III- Red,Green,Near-Infrared,Middle-Infrared SAR Images- RS1(Low incidence) RS2(High Incidence) DEM(Digital Elevation Model)
Resolution – 25X25 m
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Satellite & 3-D View of Alwar
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DATA SET
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RESOLVING THE MIXED PIXEL Satellite Image
1)Identify the Terrain features present in Image (Data set of pure pixels) and the classes of mixed pixel (Data set of Mixed pixels)
Therefore, Each of the mixed pixel corresponds to exactly two of the terrain features.
2)Consider each Terrain feature as Universal Habitat(that comprises of pure pixels). Calculate HSI of each of the Habitat.
[Initially HSI is mean of standard deviation]
3) Take one class of Mixed pixel and transfer each of corresponding mixed pixel to both the Habitats(Terrain feature) to
which it belongs i.e. Immigration & Emigration
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RESOLVING THE MIXED PIXEL
4) Recalculate the HSI of those two Habitats
If recalculated HSIA<HSIB
Absorb the mixed pixel in Feature A and PPIA ++
Absorb the mixed pixel in Feature B and PPIB++
True False
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5) Repeat till all the mixed pixels of class taken are resolved
6) Go to step 3 until all classes of mixed pixels are taken and resolved.
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PPI-Pure Pixel Index /HSI
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Water
Vegetation
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Water Pixels- 3,5,7,9
Vegetation Pixels-1,2,4,6,8
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•BBO efficiently resolves the mixed pixel & can also be used for other class types.
•BBO mixed pixel resolution algorithm also helps in improving the image classification accuracy and feature extraction.
•Increases the accuracy for the target recognition for air strikes & Defense purpose .
•Can be used for uncovering the enemy camps using the Ariel images.
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[1] Ralph W.Kiefer, Thomes M. Lillesand, “Principles of Remote Sensing”,2006.
[2] V.K.Panchal , Sonakshi Gupta, Nitish Gupta, Mandira Monga “Eliciting conflicts in expert’s decision for land use classification”, International Conference on Environment Engineering and Applications, Singapore, pp. 30-33, 2010.
[3] A. Wallace,“The Geographical Distribution of Animals (Two Volumes)”.Boston, MA: Adamant Media Corporation, 2005.
[4] C. Darwin, “The Origin of Species. New York: Gramercy”, 1995.
[5] R. MacArthur and E. Wilson, “The Theory of Biogeography”.Princeton, NJ: Princeton Univ. Press, 1967.
[6] Dan Simon, “Biogeography based optimization”. : IEEE transactions on evolutionary computation, vol. 12, no. 6, December 2008
[7] P. Fisher,”The Pixel: a Snare or a Delusion”, International Journal of Remote Sensing, Vol.18: pp. 679-685, 1997.
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Saturday, February 05,2011
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NITISH GUPTA ([email protected],[email protected])
V.K.PANCHAL([email protected])
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