1. Artificial Intelligence For Mixed Pixel Resolution By Nitish Gupta (Guru Gobind Singh Indraprastha University) Dr. V.K. Panchal (Defence Research Development Organization) 1

2.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & ReferencesOutline 27-JULY-2011 IGARSS,2011-VANCOUVER 2

3. Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 3 IGARSS,2011-VANCOUVER  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 ? \ 27-JULY-2011

4.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 100Meter resolution Patalganga, India 4 27-JULY-2011 IGARSS,2011-VANCOUVER

5.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 5Meter resolution Patalganga, India 5 27-JULY-2011 IGARSS,2011-VANCOUVER

6.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 6 1. Mixed pixel due to the presence of small, sub-pixel targets within the area it represents. 27-JULY-2011 IGARSS,2011-VANCOUVER

7.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 7 2. Mixing as a result of the pixel straddling the boundary of discrete thematic classes. 27-JULY-2011 IGARSS,2011-VANCOUVER

8.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 8 3. Mixing due to gradual transition observed between continuous thematic classes. 27-JULY-2011 IGARSS,2011-VANCOUVER Aral Sea

9.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References SPATIAL RESOLUTION & MIXED PIXEL 9 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 !! 27-JULY-2011 IGARSS,2011-VANCOUVER

10. \\  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References \\  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 10 When two distinct objects display similar spectral signatures / Fingerprints 27-JULY-2011 IGARSS,2011-VANCOUVER

11. \\  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References \\  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 11 27-JULY-2011 IGARSS,2011-VANCOUVER

12.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 12 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. 27-JULY-2011 IGARSS,2011-VANCOUVER

13. 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.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 13 27-JULY-2011 IGARSS,2011-VANCOUVER

14.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 14 TERRAIN FEATURES RADIO SPECTROMETER SPECTRAL SIGNATURES BIO-GEOGRAPHY BASED OPTIMIZATION DOMAIN EXPERT 1 2 3 4 5 MIXED PIXEL RESOLVED 6 27-JULY-2011 IGARSS,2011-VANCOUVER

15.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 15 ANALYSING MULTISPECTRAL IMAGE OF ALWAR (RAJASTHAN, INDIA) False Color Composition Image 27-JULY-2011 IGARSS,2011-VANCOUVER

16.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 16 27-JULY-2011 IGARSS,2011-VANCOUVER 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

17.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 17 Satellite & 3-D View of Alwar 27-JULY-2011 IGARSS,2011-VANCOUVER

18.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 18 DATA SET 27-JULY-2011 IGARSS,2011-VANCOUVER

19.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 19 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 C 27-JULY-2011 IGARSS,2011-VANCOUVER

20.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 20 RESOLVING THE MIXED PIXEL 4) Recalculate the HSI of those two Habitats If recalculated HSI A <HSI B Absorb the mixed pixel in Feature A and PPI A ++ Absorb the mixed pixel in Feature B and PPI B ++ True False C 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. O 27-JULY-2011 IGARSS,2011-VANCOUVER PPI-Pure Pixel Index /HSI

21.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 21 Water Vegetation 27-JULY-2011 IGARSS,2011-VANCOUVER

22. JULY,27,2011  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 22 Water Pixels- 3,5,7,9 Vegetation Pixels-1,2,4,6,8 IGARSS,2011-VANCOUVER

23.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 23 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. 27-JULY-2011 IGARSS,2011-VANCOUVER

24.  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References  Motivation  Spatial Resolution & Mixed Pixel  Expert’s Opinion & Class Conflict  Technology  Proposed Methodology  Case Study  Result & References 24 [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. 27-JULY-2011 IGARSS,2011-VANCOUVER

25. Saturday, February 05,2011 25 NITISH GUPTA (ntshgpt@gmail.com,ntshgpt@yahoo.com) V.K.PANCHAL (vkpans@gmail.com) 27-JULY-2011 IGARSS,2011-VANCOUVER