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

2. Outline 27-JULY-2011 IGARSS,2011-VANCOUVER

6. SPATIAL RESOLUTION & MIXED PIXEL 1. Mixed pixel due to the presence of small, sub-pixel targets within the area it represents . 27-JULY-2011 IGARSS,2011-VANCOUVER

7. SPATIAL RESOLUTION & MIXED PIXEL 2. Mixing as a result of the pixel straddling the boundary of discrete thematic classes . 27-JULY-2011 IGARSS,2011-VANCOUVER

8. SPATIAL RESOLUTION & MIXED PIXEL 3. Mixing due to gradual transition observed between continuous thematic classes . 27-JULY-2011 IGARSS,2011-VANCOUVER Aral Sea

9. SPATIAL RESOLUTION & MIXED PIXEL 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. When two distinct objects display similar spectral signatures / Fingerprints 27-JULY-2011 IGARSS,2011-VANCOUVER

11. 27-JULY-2011 IGARSS,2011-VANCOUVER

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. ANALYSING MULTISPECTRAL IMAGE OF ALWAR (RAJASTHAN, INDIA) False Color Composition Image 27-JULY-2011 IGARSS,2011-VANCOUVER

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. Satellite & 3-D View of Alwar 27-JULY-2011 IGARSS,2011-VANCOUVER

18. DATA SET 27-JULY-2011 IGARSS,2011-VANCOUVER

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. 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. Water Vegetation 27-JULY-2011 IGARSS,2011-VANCOUVER

22. JULY,27,2011 Water Pixels- 3,5,7,9 Vegetation Pixels-1,2,4,6,8 IGARSS,2011-VANCOUVER

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