Dr. Richard B. Gomez, Instructor

Dr. Richard B. Gomez, Instructor PowerPoint PPT Presentation


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What is Hyperspectral Image Data?Interpretation of Digital Image DataPixel ClassificationHSI Data Processing Techniques Methods and Algorithms (Continued)Principal Component AnalysisUnmixing Pixel ProblemSpectral Mixing AnalysisOtherFeature Extraction TechniquesN-dimensional ExploitationCluster Analysis.

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Dr. Richard B. Gomez, Instructor

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2. What is Hyperspectral Image Data? Interpretation of Digital Image Data Pixel Classification HSI Data Processing Techniques Methods and Algorithms (Continued) Principal Component Analysis Unmixing Pixel Problem Spectral Mixing Analysis Other Feature Extraction Techniques N-dimensional Exploitation Cluster Analysis Outline

3. What is Hyperspectral Image Data?

5. Interpretation of Digital Image Data

8. Finding Optimal Feature Subspaces

10. Pixel Classification

11. Pixel Classification

12. Pixel Classification

23. Linear Spectral Unmixing (LSU) Generates maps of the fraction of each endmember in a pixel Orthogonal Subspace Projection (OSP) Suppresses background signatures and generates fraction maps like the LSU algorithm Spectral Angle Mapper (SAM) Treats a spectrum like a vector; Finds angle between spectra Minimum Distance (MD) A simple Gaussian Maximum Likelihood algorithm that does not use class probabilities Binary Encoding (BE) and Spectral Signature Matching (SSM) Bit compare simple binary codes calculated from spectra

26. Supervised Classification

27. Parallelepiped

28. Maximum Likelihood

29. Minimum Distance

30. Euclidean Distance

31. Mahalanobis Distance

47.

48.

49. The mean of the original data is the origin of the transformed system with the transformed axes of each component mutually orthogonal To begin the transformation, the covariance matrix, C, is found. Using the covariance matrix, the eigenvalues, ?i, are obtained from |C – ?iI| = 0 where i = 1,2,...,n (n is the total number of original images and I is an identity matrix)

50. The eigenvalues, ?i,, are equal to the variance of each corresponding component image The eigenvectors, ei , define the axes of the components and are obtained from (C – ?iI) ei = 0 The principal components are then given as PC = T• DN where DN is the digital number matrix of the original data and T is the (n x n) transformation matrix with matrix elements given by eij , i, j = 1,2,3,...n

52. Mean and Variance

61. High dimensional space is mostly empty. Data in high dimensional space is mostly in a lower dimensional structure.

64. Define Desired Classes

67. Remote sensing by airborne or spaceborne hyperspectral sensors Finite flux reaching sensor causes spatial-spectral resolution trade-off Hyperspectral data has hundreds of bands of spectral information Spectrum characterization allows subpixel analysis and material identification

68. Assumes reflectance from each pixel is caused by a linear mixture of subpixel materials

70. Constraint Conditions

71. Unmixes broad material classes first Proceeds to a group’s constituents only if the unmixed fraction is greater than a given threshold

73. Compare squared error from traditional, stepwise and hierarchical methods Visually assess fraction maps for accuracy

74. Endmembers are simply material types Broad classification: road, grass, trees… Fine classification: dry soil, moist soil... Use image-derived endmembers to produce spectral library Average reference spectra from “pure” sample pixels Chose specific number of distinct endmembers

77. Linear unmixing does poorly, forcing fractions for all materials Hierarchical approach performs better but requires extensive user involvement Stepwise routine succeeds using adaptive endmember selection without extra preparation

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