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Hyperspectral Image Classification

Hyperspectral Image Classification. Jonatan Gefen 28/11/2012. Outline. Introduction to classification Whole Pixel Subpixel Classification Linear Unmixing Matched Filtering (partial  unmixing ) More Classification techniques. Image Classification. Spatial Classification

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Hyperspectral Image Classification

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  1. Hyperspectral Image Classification Jonatan Gefen 28/11/2012

  2. Outline • Introduction to classification • Whole Pixel • Subpixel Classification • Linear Unmixing • Matched Filtering (partial unmixing) • More Classification techniques

  3. Image Classification • Spatial Classification • Spectral Classification

  4. Image Classification • Spatial Image classification: • Based on the structures in the image (clear edges) • Based on neighbor pixels • Depends on the spatial resolution • Can be done manually

  5. Image Classification • Spectral Image classification: • Increase of information per pixel • Increase of dimensionality • Can’t be done manually (but can be done Automatically) • Based on spectral sig. • Based on single pixel

  6. Spectral Classification • Whole Pixel • Sub-Pixel • Others (advanced)

  7. Supervised / Unsupervised • Based on known a priori through a combination of fieldwork, map analysis, and personal experience • On-screen selection of polygonal training data (ROI), and/or • On-screen seeding of training • The seed program begins at a single x, y location • Expands as long as it finds pixels with spectral similar to the original seed pixel • This is a very effective way of collecting homogeneous training information • From spectral library of field measurements

  8. Whole Pixel classification • Assumes that each pixel contains single material and noise • Tries to determine if a Target is in the pixel

  9. Whole Pixel classification • Euclidean Distance • SAM • Spectral Feature Fitting

  10. Sub-pixel • Tries to measure the abundance of the Target in the pixel • Assumes that a pixel can represent more than one material

  11. Sub-pixel • Linear Unmixing • Filter Match

  12. Spectral classification • Definitions: • Target • Endmember • Infeasibility

  13. Linear Unmixing A model assumption that each pixel is a Linear-Combination of materials – is the pixel value at band – spectral value of the endmember – the abundance factor of the endmember – noise

  14. Linear Unmixing Linear Unmixing is trying to solve linear equations to find possible endmembers and their fraction of the pixel. – the number of bands

  15. General Linear Unmixing • Minimizing: • Find Least min square.

  16. L1 Unmixing • Assumes that all the elements are non negative. • Minimizing: • - called regulator • Using NMF (Nonnegative matrix factorization)

  17. NMF(original form) • Algorithm: • (in our case already known)

  18. Match Filter(Partial Unmixing) • This technique is used to find specific Targets in the image only user chosen targets are mapped. • Matched Filtering “filters” the input image for good matches to the chosen target spectrum • The technique is best used on rare Targets in the image.

  19. Match Filter(Partial Unmixing) • Likelihood Ratio • Using a threshold to decide if signal is present at the pixel.

  20. Match Filter(Partial Unmixing) • The Matched Filter result calculation: • The T(x) will hold the MF value of the endmember at pixel x if > 0 the endmember present.

  21. MNF (Minimum Noise Fraction) • Λ is a diagonal matrix containing the eigenvalues corresponding to V • MNF: • is the covariance matrix of the signal (generally taken to be the covariance matrix of the image) • is the covariance matrix of the noise (can be estimated using various procedures)

  22. Match Filter(Partial Unmixing) • Mixture-Tuned Matched Filtering • matched filter vector • - MNF Covariance matrix • the target vector in MNF space

  23. Match Filter(Partial Unmixing) • infeasibility value • the interpolated vector of eigenvalues • the target vector component • - the MNF spectra for pixel

  24. After filter result

  25. More techniques • Non-linear mixing

  26. Linear unmixing • Non Linear unmixing

  27. Sub-Pixel Summery • Can allow search of item that is a very small part of a given pixel • Can give data about abundance of Targets • Issues: • Highly dependent on the contrast of the target to the background of the pixel • One potential problem with Matched Filtering is that it is possible to end up with false positive results

  28. More techniques • Spatial-spectral classification

  29. References • N. Keshava - “A Survey of Spectral Unmixing Algorithms” • P. Shippert, “Introduction to Hyperspectral Image Analysis” , Earth Science Applications Specialist Research Systems, Inc. • Uttam Kumar, Norman Kerle , and Ramachandra T V – “Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data” • YuliyaTarabalka, JónAtliBenediktsson , Jocelyn Chanussot, James C. Tilton – “Hyperspectral Data Classification Using Spectral-Spatial Approaches” • Jacob T. Mundt, David R. Streutker, Nancy F. Glenn – “PARTIAL UNMIXING OF HYPERSPECTRAL IMAGERY: THEORY AND METHODS “ • B. Ball, A. Brooks, A. Langville - Nonnegative matrix factorization • Z. Guo, T. Wittmanand S. Osher - L1 Unmixing and its Application to HyperspectralImage Enhancement

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