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#### Presentation Transcript

**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