Principal Component Analysis Toolbox
Features:
Transforms the data to a lower dimensional space and finds principal component vectors with their directions along the maximum variances of the input bands. The principal components are in descending order of the amount of total variance.
Identifies the principal components from the hyperspectral data.
Combines principal components in a pseudo color image.
Plots spectra of components.
Plots Cumulative Fraction of Variance.
Quantifies the area by each component.
Saves PCA data in IDCube format.
Steps:
1. Select Principal Component Analysis from the Toolbox tabs from the Main Interface.
2. In the COMPONENT SELECTION panel, scroll through different principal components assigned to different color channels. (Default Component #1 is red, component #2 is green, and component #3 is blue). Note: Only the first 20 principal components can be accessed.
3. Assign Weight to any of the channels to enhance the signal at a specific channel. Default values = 1 for each channel.
4. Image enhancement enables improved visualization. Current options are:
a. Contrast Saturation Limit (%). The default value is 0. The range is from 0 to 49%.
b. Gamma Correction. Gamma Correction takes values between 0 and infinity. The default value is 1. If gamma is <1, the image is weighted toward brighter pixels, if gamma is >1, the image is weighted toward darker pixels.
5. Click Quantify to open a dialog with the following options.
6. (Optional) Set Threshold value, %. The default level is 30% and will be applied to each component. You do not need to adjust the value; you can do that in the next step.
a. Select the Principal Components Number(s) that you would like to quantify. Use the format such as 1 2 3 4 with spaces between the numbers. Note: The limit is 10 components.
b. Select an Overlay method. The available options are:
i. SAM (Spectral Angle Map) – default.
ii. SID (Spectral Information Divergence).
iii. SIDSAM (Spectral Information Divergence-Spectral Angle Mapper Hybrid method).
7. Click Apply Overlay. The new pop-up PCA Quantification will show the following information:
a. Pseudo RGB rendering of the original dataset.
b. Threshold values, % selection box for individual Principal Component (the limit is 10 components).
c. Threshold Segmented Regions (based on the input of the threshold value) image with the overlaid principal components.
d. Histograms that correspond to each of the components.
e. Area of each component in pixels (located in the title of each histogram plot component).
8. Adjust the Threshold value for each component. Note: The colors of each component might overlay with the map of the early listed principal component number. To verify overlap, assign the previous component Threshold value to 0.
Additional Information:
Each image can be zoomed and panned by using a mouse. Each image can be also modified using shortcuts.
You can click on each image to see it in a larger separate window. Each image can be saved.
The toolbox includes the following algorithms: a) PCA and b) Overlay which includes three algorithms: SAM, SID, and SAMSID. The calculations are similar to the abundance method used in the endmembers mapping but using principal components instead of endmembers.
References:
SAM: Kruse, F. A., A. B. Lefkoff, J. B. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz. "The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer Data." Remote Sensing of Environment 44 (1993): 145-163.
SID: Chein-I Chang. “An Information-Theoretic Approach to Spectral Variability, Similarity, and Discrimination for Hyperspectral Image Analysis.” IEEE Transactions on Information Theory 46, no. 5 (August 2000): 1927–32. https://doi.org/10.1109/18.857802.
SIMSAD: Chang, Chein-I. “New Hyperspectral Discrimination Measure for Spectral Characterization.” Optical Engineering 43, no. 8 (August 1, 2004): 1777. https://doi.org/10.1117/1.1766301.
