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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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